run-20260616-154816Z / geo-l3-antarctica-stations-geodesic

0:00

I need to put together the cross-station logistics-overlap maps for the Antarctic Treaty consultative meeting next month. Each Antarctic research station has a notional 200 km operational radius. Please pull stations from Overture south of -60 latitude, along with the Antarctic landmass and water features. For the first output I need the land-clipped station zones grouped into coalitions wherever they overlap. Each coalition gets an integer `coalition` id, and that column has to take more than one distinct value across the dataset. Every feature needs `station_id` and `station_name` as well. For the second output I need the over-water portion of each station's zone, attributed with the water features it intersects. Each row in that file must carry `station_id`, `station_name`, `water_id`, `water_name`, `water_subtype`, and a `water_source` column whose values are the literal Overture theme names `base.water` or `base.bathymetry`. Please write both as GeoParquet in an appropriate projected coordinate system for Antarctica, with MultiPolygon geometry: `station_spheres.geoparquet` for the land-clipped coalition polygons, and `station_water_overlap.geoparquet` for the per-station over-water intersections with the water attribution described above.

0:00

System info

{
  "adapter": "openrouter",
  "model": "deepseek/deepseek-v4-flash",
  "base_url": "https://openrouter.ai/api/v1"
}

0:14

Let me break down this task:

1. Get Antarctic research stations from Overture (south of -60 latitude)
2. Get Antarctic landmass and water features
3. Create 200 km operational radius zones around each station
4. Clip these zones to land areas and group into coalitions (overlapping zones)
5. Create output 1: `station_spheres.geoparquet` - land-clipped coalition polygons
6. Create output 2: `station_water_overlap.geoparquet` - over-water portions intersecting water features

Let me start by exploring what Overture data is available and how to query it.

I'll use overturemaps library which is pre-installed. Let me write a comprehensive Python script.

First, let me check what's available in the working directory and verify the tools.

0:14

Bash ls /work/

(no output)

0:14

Bash python -c "import overturemaps; help(overturemaps)" 2>&1 | head -60

Help on package overturemaps:

NAME
    overturemaps

PACKAGE CONTENTS
    __main__
    changelog
    cli
    core
    models
    releases
    state
    writers

FILE
    /usr/local/lib/python3.14/site-packages/overturemaps/__init__.py

0:27

Let me understand the task:

1. Pull Antarctic research stations from Overture south of -60 latitude
2. Pull Antarctic landmass and water features
3. Create 200 km operational radius buffers around each station
4. Clip the buffers to land (Antarctic landmass) → these form "station spheres" grouped into coalitions where they overlap
5. Identify over-water portions of each station's zone, attributed with water features they intersect
6. Output both as GeoParquet in an appropriate projected coordinate system for Antarctica

Let me start by exploring Overture Maps data to understand what themes and types are available.

Appropriate projected coordinate system for Antarctica: EPSG:3031 (Antarctic Polar Stereographic) or EPSG:6932 (also Antarctic Polar Stereographic). EPSG:3031 is the standard.

Let me explore Overture first.

0:27

Bash cd /work && python -c "

cd /work && python -c "
import overturemaps
# List available release themes
print('Available releases:', overturemaps.available_releases()[:5])
print()
# Look at core module for available tables/themes
import overturemaps.core as core
print(dir(core))
" 2>&1

Traceback (most recent call last):
  File "<string>", line 4, in <module>
    print('Available releases:', overturemaps.available_releases()[:5])
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: module 'overturemaps' has no attribute 'available_releases'

0:28

Bash cd /work && python -c "

cd /work && python -c "
import overturemaps

# Record types - look at what's available
# Try getting a small sample of places (which includes research stations usually)
# or buildings
# Let's look at the overturemaps CLI help for record options
import subprocess
subprocess.run(['overturemaps', '--help'], capture_output=False)
" 2>&1

Usage: overturemaps [OPTIONS] COMMAND [ARGS]...

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  changelog  Query the GERS changelog for feature changes.
  download
  gers       Query the GERS registry for a feature by its GERS ID.
  releases   Manage and query Overture Maps releases.

0:39

Let me look at the overturemaps API more carefully.

0:39

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import available_releases
print(available_releases()[:5])
" 2>&1

Traceback (most recent call last):
  File "<string>", line 2, in <module>
    from overturemaps.core import available_releases
ImportError: cannot import name 'available_releases' from 'overturemaps.core' (/usr/local/lib/python3.14/site-packages/overturemaps/core.py)

0:40

Bash cd /work && python -c "

cd /work && python -c "
# Check what download does
from overturemaps import cli
cli.download --help
" 2>&1

Traceback (most recent call last):
  File "<string>", line 4, in <module>
    cli.download --help
                  ^^^^^
TypeError: bad operand type for unary -: '_Helper'

0:40

Bash cd /work && overturemaps download --help 2>&1

Usage: overturemaps download [OPTIONS]

Options:
  --bbox BBOX
  -f [geojson|geojsonseq|geoparquet]
                                  [required]
  -o, --output PATH
  -t, --type [address|bathymetry|building|building_part|division|division_area|division_boundary|place|segment|connector|infrastructure|land|land_cover|land_use|water]
                                  [required]
  -r, --release TEXT              Release version (defaults to latest)
  --stac / --no-stac              By default, uses the STAC catalog to limit
                                  which Parquet files are downloaded. Pass
                                  --no-stac to skip the catalog and read the
                                  full S3 dataset directly.
  --connect_timeout INTEGER
  --request_timeout INTEGER
  --help                          Show this message and exit.

0:47

Now I can see the available types. I need:

1. **Places** - for research stations (type `place`)
2. **Division** - for Antarctic landmass (type `division` or `division_area`)
3. **Water** - for water features (type `water`)
4. **Bathymetry** - for bathymetry (type `bathymetry`)

Let me look at what's in the places data - I need to find research stations south of -60 latitude. Let me also look at the divisions to find Antarctica.

Actually, for "stations", the Overture `place` theme has categories like "research_station" or similar. Let me explore.

0:47

Bash cd /work && python -c "

cd /work && python -c "
import overturemaps

# Look at the core module to understand the API
import inspect
from overturemaps import core
print(inspect.getsource(core))
" 2>&1 | head -100

import io
import json
import sys
from typing import List, Optional, Tuple
from urllib.request import urlopen

import pyarrow as pa
import pyarrow.compute as pc
import pyarrow.dataset as ds
import pyarrow.fs as fs
import pyarrow.parquet as pq

from .models import BBox

STAC_CATALOG_URL = "https://stac.overturemaps.org/catalog.json"

# Cache for STAC catalog to avoid repeated network calls
_cached_stac_catalog = None

# Allows for optional import of additional dependencies
try:
    from geopandas import GeoDataFrame

    HAS_GEOPANDAS = True
except ImportError:
    HAS_GEOPANDAS = False

    class GeoDataFrame:
        pass


def _get_stac_catalog() -> dict:
    """
    Fetch and cache the STAC catalog.

    Returns
    -------
    dict: The STAC catalog JSON
    """
    global _cached_stac_catalog

    if _cached_stac_catalog is not None:
        return _cached_stac_catalog

    try:
        with urlopen(STAC_CATALOG_URL) as response:
            catalog = json.load(response)

        # Cache the catalog
        _cached_stac_catalog = catalog
        return catalog

    except Exception as e:
        raise Exception(f"Could not fetch STAC catalog: {e}") from e


def get_available_releases() -> Tuple[List[str], str]:
    """
    Fetch available releases from the STAC catalog.

    Returns
    -------
    Tuple of (all_releases, latest_release) where:
        - all_releases is a list of release version strings
        - latest_release is the latest release version string
    """
    catalog = _get_stac_catalog()

    latest_release = catalog.get("latest")

    # Extract release versions from the child links
    releases = []
    for link in catalog.get("links", []):
        if link.get("rel") == "child":
            href = link.get("href", "")
            # href format is "./2025-09-24.0/catalog.json"
            release_version = href.strip("./").split("/")[0]
            if release_version:
                releases.append(release_version)

    return releases, latest_release


def get_latest_release() -> str:
    """
    Get the latest release version.

    Returns
    -------
    str: The latest release version
    """
    _, latest = get_available_releases()
    return latest


# For backwards compatibility, expose ALL_RELEASES as a list
# This will be populated dynamically when first accessed
def _get_all_releases():
    releases, _ = get_available_releases()
    return releases

0:52

Let me understand the overturemaps API better by looking at more of the core module.

0:52

Bash cd /work && python -c "

cd /work && python -c "
import inspect
from overturemaps import core
print(inspect.getsource(core))
" 2>&1 | tail -n +100

    return releases


# Lazy evaluation property-like access
class _ReleasesProxy:
    def __getitem__(self, index):
        return _get_all_releases()[index]

    def __iter__(self):
        return iter(_get_all_releases())

    def __len__(self):
        return len(_get_all_releases())

    def __repr__(self):
        return repr(_get_all_releases())


ALL_RELEASES = _ReleasesProxy()


def _coerce_bbox(
    bbox: BBox | tuple[float, float, float, float] | list[float] | None,
) -> BBox | None:
    """Normalize bbox input to a BBox instance."""
    if bbox is None:
        return None

    if isinstance(bbox, BBox):
        return bbox

    if len(bbox) != 4:
        raise ValueError("bbox must contain exactly 4 numeric values")

    return BBox(
        xmin=float(bbox[0]),
        ymin=float(bbox[1]),
        xmax=float(bbox[2]),
        ymax=float(bbox[3]),
    )


def _get_files_from_stac(
    theme: str, overture_type: str, bbox: BBox, release: str
) -> Optional[List[str]]:
    """
    Returns a list of bucket/key paths using the STAC-geoparquet index
    """
    stac_url = f"https://stac.overturemaps.org/{release}/collections.parquet"
    try:
        # Arrow can't read HTTP URLs directly; read into memory first
        with urlopen(stac_url) as response:
            data = response.read()
            buffer = io.BytesIO(data)
            stac_table = pq.read_table(buffer)

        feature_type_filter = (pc.field("collection") == overture_type) & (
            pc.field("type") == "Feature"
        )

        xmin, ymin, xmax, ymax = bbox.as_tuple()
        bbox_filter = (
            (pc.field("bbox", "xmin") < xmax)
            & (pc.field("bbox", "xmax") > xmin)
            & (pc.field("bbox", "ymin") < ymax)
            & (pc.field("bbox", "ymax") > ymin)
        )

        combined_filter = feature_type_filter & bbox_filter
        table = stac_table.filter(combined_filter)

        if table.num_rows > 0:
            file_paths = table.column("assets").to_pylist()

            # clip out the "s3://" prefix
            s3_paths = [
                path["aws"]["alternate"]["s3"]["href"][len("s3://") :]
                for path in file_paths
            ]
            return s3_paths
        else:
            print(
                f"No data found for release {release} in query bbox {bbox.as_tuple()}."
            )
            return []

    except Exception as e:
        print(f"Error reading STAC index at {stac_url}: {e}")
        return None


def _record_batch_reader_from_dataset(
    dataset: ds.Dataset,
    filter_expr=None,
) -> Optional[pa.RecordBatchReader]:
    """
    Create a RecordBatchReader from an S3 dataset with optional filtering.

    Parameters
    ----------
    dataset: pyarrow dataset
        Dataset to read from
    filter_expr: pyarrow expression, optional
        Filter to apply when reading the dataset

    Returns
    -------
    RecordBatchReader with the feature data, or None if error occurs
    """
    try:
        batches = dataset.to_batches(
            filter=filter_expr,
            use_threads=True,
            batch_readahead=16,
            fragment_readahead=4,
        )

        # Filter out empty batches to avoid downstream issues
        non_empty_batches = (b for b in batches if b.num_rows > 0)

        geoarrow_schema = geoarrow_schema_adapter(dataset.schema)
        return pa.RecordBatchReader.from_batches(geoarrow_schema, non_empty_batches)

    except Exception as e:
        print(f"Error reading dataset: {e}")
        return None


def _prepare_query(
    overture_type,
    bbox: BBox | tuple[float, float, float, float] | list[float] | None = None,
    release=None,
    connect_timeout=None,
    request_timeout=None,
    stac=False,
) -> Optional[Tuple[ds.Dataset, Optional[pc.Expression]]]:
    """
    Resolve the S3 dataset and filter expression for a given query.

    Returns the dataset and filter expression ready for counting or streaming,
    or None if STAC reports no files intersect the bbox.
    """
    if release is None:
        release = get_latest_release()
    path = _dataset_path(overture_type, release)
    bbox_obj = _coerce_bbox(bbox)

    intersecting_files = None
    if bbox_obj and stac:
        intersecting_files = _get_files_from_stac(
            type_theme_map[overture_type], overture_type, bbox_obj, release
        )
        if intersecting_files is not None and len(intersecting_files) == 0:
            return None

    if bbox_obj:
        xmin, ymin, xmax, ymax = bbox_obj.as_tuple()
        filter_expr = (
            (pc.field("bbox", "xmin") < xmax)
            & (pc.field("bbox", "xmax") > xmin)
            & (pc.field("bbox", "ymin") < ymax)
            & (pc.field("bbox", "ymax") > ymin)
        )
    else:
        filter_expr = None

    dataset = ds.dataset(
        intersecting_files if intersecting_files is not None else path,
        filesystem=fs.S3FileSystem(
            anonymous=True,
            region="us-west-2",
            connect_timeout=connect_timeout,
            request_timeout=request_timeout,
        ),
    )

    return dataset, filter_expr


def count_rows(
    overture_type,
    bbox=None,
    release=None,
    connect_timeout=None,
    request_timeout=None,
    stac=False,
) -> int:
    """Return the number of rows matching the given parameters."""
    result = _prepare_query(
        overture_type, bbox, release, connect_timeout, request_timeout, stac
    )
    if result is None:
        return 0
    dataset, filter_expr = result
    return dataset.count_rows(filter=filter_expr)



def record_batch_reader(
    overture_type,
    bbox=None,
    release=None,
    connect_timeout=None,
    request_timeout=None,
    stac=False,
) -> Optional[pa.RecordBatchReader]:
    """Return a pyarrow RecordBatchReader for the desired bounding box and s3 path, or None on error."""
    result = _prepare_query(
        overture_type, bbox, release, connect_timeout, request_timeout, stac
    )
    if result is None:
        return None
    dataset, filter_expr = result
    return _record_batch_reader_from_dataset(dataset, filter_expr=filter_expr)


def geodataframe(
    overture_type: str,
    bbox: BBox | tuple[float, float, float, float] | list[float] | None = None,
    release: str = None,
    connect_timeout: int = None,
    request_timeout: int = None,
    stac: bool = False,
) -> GeoDataFrame:
    """
    Loads geoparquet for specified type into a geopandas dataframe

    Parameters
    ----------
    overture_type: type to load
    bbox: optional bounding box for data fetch (xmin, ymin, xmax, ymax)
    connect_timeout: optional connection timeout in seconds
    request_timeout: optional request timeout in seconds

    Returns
    -------
    GeoDataFrame with the optionally filtered theme data

    """
    if not HAS_GEOPANDAS:
        raise ImportError("geopandas is required to use this function")

    reader = record_batch_reader(
        overture_type,
        bbox=bbox,
        release=release,
        connect_timeout=connect_timeout,
        request_timeout=request_timeout,
        stac=stac,
    )
    return GeoDataFrame.from_arrow(reader)


def geoarrow_schema_adapter(schema: pa.Schema) -> pa.Schema:
    """
    Convert a geoarrow-compatible schema to a proper geoarrow schema

    This assumes there is a single "geometry" column with WKB formatting

    Parameters
    ----------
    schema: pa.Schema

    Returns
    -------
    pa.Schema
    A copy of the input schema with the geometry field replaced with
    a new one with the proper geoarrow ARROW:extension metadata

    """
    geometry_field_index = schema.get_field_index("geometry")
    geometry_field = schema.field(geometry_field_index)
    geoarrow_geometry_field = geometry_field.with_metadata(
        {b"ARROW:extension:name": b"geoarrow.wkb"}
    )

    geoarrow_schema = schema.set(geometry_field_index, geoarrow_geometry_field)

    return geoarrow_schema


type_theme_map = {
    "address": "addresses",
    "bathymetry": "base",
    "building": "buildings",
    "building_part": "buildings",
    "division": "divisions",
    "division_area": "divisions",
    "division_boundary": "divisions",
    "place": "places",
    "segment": "transportation",
    "connector": "transportation",
    "infrastructure": "base",
    "land": "base",
    "land_cover": "base",
    "land_use": "base",
    "water": "base",
}


def _dataset_path(overture_type: str, release: str) -> str:
    """
    Returns the s3 path of the Overture dataset to use. This assumes overture_type has
    been validated, e.g. by the CLI

    """
    # Map of sub-partition "type" to parent partition "theme" for forming the
    # complete s3 path. Could be discovered by reading from the top-level s3
    # location but this allows to only read the files in the necessary partition.
    theme = type_theme_map[overture_type]
    return (
        f"overturemaps-us-west-2/release/{release}/theme={theme}/type={overture_type}/"
    )


def get_all_overture_types() -> List[str]:
    return list(type_theme_map.keys())


# Registry manifest is now part of the STAC catalog
# Access via catalog.json -> registry property -> manifest field


def _binary_search_manifest(
    manifest_tuples: List[Tuple[str, str]], gers_id: str
) -> Optional[str]:
    """
    Binary search through manifest tuples to find the file containing the given GERS ID.

    Parameters
    ----------
    manifest_tuples: List of (filename, max_id) tuples, sorted by max_id
    gers_id: The GERS ID to search for (lowercase)

    Returns
    -------
    Filename containing the ID, or None if not found
    """
    left, right = 0, len(manifest_tuples) - 1

    while left <= right:
        mid = (left + right) // 2
        filename, max_id = manifest_tuples[mid]

        if gers_id <= max_id:
            # Check if this is the first file where max_id >= gers_id
            if mid == 0 or manifest_tuples[mid - 1][1] < gers_id:
                return filename
            else:
                # Search in the left half
                right = mid - 1
        else:
            # Search in the right half
            left = mid + 1

    return None


def query_gers_registry(gers_id: str) -> Optional[Tuple[str, BBox | None]]:
    """
    Query the GERS registry to get the filepath and bbox for a given GERS ID.

    The registry always uses the latest release.

    Parameters
    ----------
    gers_id: The GERS ID to look up

    Returns
    -------
    Tuple of (filepath, bbox) where bbox is a BBox, or None if not found
    """
    import sys

    release = get_latest_release()
    release_path = f"overturemaps-us-west-2/release/{release}"
    gers_id_lower = gers_id.lower()

    try:
        # Get the cached STAC catalog
        catalog = _get_stac_catalog()

        # Get the registry object from the catalog
        registry = catalog.get("registry")
        if registry is None:
            print("Registry configuration not found in STAC catalog", file=sys.stderr)
            return None

        # The registry contains 'path' and 'manifest'
        # manifest is a list of [filename, max_id] tuples
        registry_path = registry.get("path", "")
        manifest_tuples = registry.get("manifest", [])

        if not manifest_tuples:
            print("Registry manifest is empty in STAC catalog", file=sys.stderr)
            return None

        # Use binary search to find the file containing this GERS ID
        registry_file = _binary_search_manifest(manifest_tuples, gers_id_lower)

        if registry_file is None:
            print(f"{gers_id} does not exist in the GERS Registry.", file=sys.stderr)
            return None

        # Read the specific registry file with filter (predicate pushdown)
        # This only reads the relevant row groups instead of the entire file
        registry_path = f"overturemaps-us-west-2/registry/{registry_file}"
        filesystem = fs.S3FileSystem(anonymous=True, region="us-west-2")

        # Use filters parameter for predicate pushdown
        filtered_table = pq.read_table(
            registry_path, filesystem=filesystem, filters=[("id", "=", gers_id_lower)]
        )

        if filtered_table.num_rows == 0:
            print(f"{gers_id} does not exist in the GERS Registry.", file=sys.stderr)
            return None

        # Get the first (should be only) result
        row = filtered_table.to_pylist()[0]
        path = row["path"]
        bbox_struct = row.get("bbox")
        version = row.get("version")
        first_seen = row.get("first_seen")
        last_seen = row.get("last_seen")
        last_changed = row.get("last_changed")

        # Check if path is NULL - means feature is not present in current release
        if path is None:
            print(
                f"GERS ID '{gers_id}' found in registry but not present in release {release}",
                file=sys.stderr,
            )
            print(f"  Version: {version}", file=sys.stderr)
            print(f"  First seen: {first_seen}", file=sys.stderr)
            print(f"  Last seen: {last_seen}", file=sys.stderr)
            if last_changed:
                print(f"  Last changed: {last_changed}", file=sys.stderr)
            return None

        # Construct full filepath
        if not path.startswith("/"):
            path = "/" + path
        filepath = f"{release_path}{path}"

        # Extract bbox values if available
        if bbox_struct is not None:
            bbox = BBox(
                xmin=bbox_struct["xmin"],
                ymin=bbox_struct["ymin"],
                xmax=bbox_struct["xmax"],
                ymax=bbox_struct["ymax"],
            )
        else:
            bbox = None

        # Write registry information to stderr
        print(f"Found GERS ID '{gers_id}' in release {release}", file=sys.stderr)
        print(f"  Version: {version}", file=sys.stderr)
        print(f"  Filepath: s3://{filepath}", file=sys.stderr)
        if bbox is not None:
            print(
                f"  Bbox: [{bbox.xmin:.6f}, {bbox.ymin:.6f}, {bbox.xmax:.6f}, {bbox.ymax:.6f}]",
                file=sys.stderr,
            )
        else:
            print(f"  Bbox: None", file=sys.stderr)
        print(f"  First seen: {first_seen}", file=sys.stderr)
        print(f"  Last seen: {last_seen}", file=sys.stderr)
        if last_changed:
            print(f"  Last changed: {last_changed}", file=sys.stderr)

        return (filepath, bbox)

    except Exception as e:
        print(f"Error querying GERS registry: {e}", file=sys.stderr)
        return None


def record_batch_reader_from_gers(
    gers_id: str,
    connect_timeout: int = None,
    request_timeout: int = None,
    registry_result: Optional[Tuple[str, BBox | None]] = None,
) -> Optional[pa.RecordBatchReader]:
    """
    Return a pyarrow RecordBatchReader for a specific GERS ID.

    The registry always uses the latest release.

    Parameters
    ----------
    gers_id: The GERS ID to look up
    connect_timeout: Optional connection timeout in seconds
    request_timeout: Optional request timeout in seconds
    registry_result: Optional pre-fetched registry result (filepath, bbox)
                    to avoid duplicate registry queries

    Returns
    -------
    RecordBatchReader with the feature data, or None if not found
    """
    # Use pre-fetched result if provided, otherwise query the registry
    if registry_result is None:
        result = query_gers_registry(gers_id)
        if result is None:
            return None
    else:
        result = registry_result

    filepath, bbox = result

    # Build filter expression based on ID and bbox (if available)
    filter_expr = pc.field("id") == gers_id.lower()

    if bbox is not None:
        xmin, ymin, xmax, ymax = bbox.as_tuple()
        bbox_filter = (
            (pc.field("bbox", "xmin") == xmin)
            & (pc.field("bbox", "ymin") == ymin)
            & (pc.field("bbox", "xmax") == xmax)
            & (pc.field("bbox", "ymax") == ymax)
        )
        filter_expr = filter_expr & bbox_filter

    try:
        dataset = ds.dataset(
            filepath,
            filesystem=fs.S3FileSystem(
                anonymous=True,
                region="us-west-2",
                connect_timeout=connect_timeout,
                request_timeout=request_timeout,
            ),
        )
    except Exception as e:
        print(f"Error opening dataset for GERS ID '{gers_id}': {e}", file=sys.stderr)
        return None
    return _record_batch_reader_from_dataset(dataset, filter_expr=filter_expr)

0:53

Bash cd /work && overturemaps releases list 2>&1

2026-05-20.0

1:03

Let me now understand the plan:

1. Get research stations from Overture - these would be in the `place` type (which includes research stations)
2. Get Antarctica landmass from `land` type
3. Get water features from `water` and `bathymetry` types
4. Everything south of -60 latitude

Let me first check what data is available for these themes. Let me start with the place data for research stations.

The bbox for everything south of -60° would be: xmin=-180, ymin=-90, xmax=180, ymax=-60

Let me first check the available columns/data for places, land, and water types.

1:03

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import get_all_overture_types
print(get_all_overture_types())
" 2>&1

['address', 'bathymetry', 'building', 'building_part', 'division', 'division_area', 'division_boundary', 'place', 'segment', 'connector', 'infrastructure', 'land', 'land_cover', 'land_use', 'water']

1:04

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe
import pyarrow as pa

# Get a small sample of places to see what columns are available
bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)
print('Columns:', list(gdf.columns))
print('Categories:', gdf['categories'].iloc[0] if 'categories' in gdf.columns else 'N/A')
print('Types:', gdf.dtypes)
print('Shape:', gdf.shape)
print()
# Filter for research stations
if 'categories' in gdf.columns:
    # Look at what categories exist
    all_cats = set()
    for cats in gdf['categories']:
        if cats:
            for c in cats:
                all_cats.add(c.get('name', '') if isinstance(c, dict) else str(c))
    print('Sample unique categories:', sorted(all_cats)[:30])
" 2>&1

Columns: ['id', 'geometry', 'categories', 'confidence', 'websites', 'emails', 'socials', 'phones', 'brand', 'addresses', 'names', 'sources', 'operating_status', 'basic_category', 'taxonomy', 'version', 'bbox']
Categories: {'primary': 'information_technology_company', 'alternate': array(['professional_services'], dtype=object)}
Types: id                       str
geometry            geometry
categories            object
confidence           float64
websites              object
emails                object
socials               object
phones                object
brand                 object
addresses             object
names                 object
sources               object
operating_status         str
basic_category           str
taxonomy              object
version                int32
bbox                  object
dtype: object
Shape: (2348, 17)

Sample unique categories: ['alternate', 'primary']

1:33

Let me look at what categories exist in the 'place' data to find research stations in Antarctica.

1:33

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

# Explore categories structure more deeply
# The categories column has 'primary' and 'alternate' 
unique_primary = set()
for cats in gdf['categories']:
    if cats and isinstance(cats, dict):
        p = cats.get('primary', '')
        if p:
            unique_primary.add(p)

print('Unique primary categories:')
for c in sorted(unique_primary):
    print(f'  {c}')
" 2>&1

Unique primary categories:
  accommodation
  active_life
  acupuncture
  agricultural_cooperatives
  agricultural_service
  agriculture
  aircraft_repair
  airport
  alcohol_and_drug_treatment_centers
  amateur_sports_team
  ambulance_and_ems_services
  antique_store
  aquatic_pet_store
  archery_range
  architectural_designer
  art_gallery
  art_school
  arts_and_crafts
  arts_and_entertainment
  astrologer
  auction_house
  audio_visual_equipment_store
  audiologist
  auto_company
  auto_detailing
  automation_services
  automotive
  automotive_dealer
  automotive_parts_and_accessories
  automotive_repair
  b2b_jewelers
  b2b_science_and_technology
  b2b_textiles
  bakery
  bank_credit_union
  banks
  bar
  barber
  beach
  beauty_and_spa
  beauty_product_supplier
  beauty_salon
  beer_bar
  bicycle_shop
  boat_parts_and_accessories
  boat_rental_and_training
  boat_service_and_repair
  bookstore
  boutique
  boxing_class
  brazilian_restaurant
  breakfast_and_brunch_restaurant
  bridal_shop
  brokers
  bubble_tea
  buddhist_temple
  buffet_restaurant
  building_supply_store
  burger_restaurant
  business_advertising
  butcher_shop
  cabin
  cafe
  campground
  campus_building
  candy_store
  car_dealer
  car_rental_agency
  car_window_tinting
  cardiologist
  caterer
  catholic_church
  central_government_office
  charity_organization
  chicken_restaurant
  childrens_clothing_store
  chinese_restaurant
  chiropractor
  chocolatier
  church_cathedral
  clothing_store
  cocktail_bar
  coffee_shop
  college_university
  commercial_industrial
  commercial_refrigeration
  commercial_vehicle_dealer
  community_center
  community_services_non_profits
  computer_coaching
  computer_hardware_company
  computer_museum
  computer_store
  construction_services
  contractor
  convenience_store
  cosmetic_and_beauty_supplies
  cosmetic_dentist
  cosmetology_school
  cottage
  cultural_center
  cupcake_shop
  dance_school
  day_care_preschool
  delicatessen
  dentist
  desserts
  diagnostic_services
  diner
  doctor
  driving_school
  dry_cleaning
  e_cigarette_store
  ear_nose_and_throat
  eat_and_drink
  education
  educational_camp
  educational_research_institute
  educational_services
  electronics
  elementary_school
  embassy
  emergency_room
  employment_agencies
  energy_company
  energy_equipment_and_solution
  engineering_services
  escape_rooms
  evangelical_church
  event_photography
  event_planning
  excavation_service
  eyewear_and_optician
  fabric_store
  family_practice
  farm
  fashion
  fashion_accessories_store
  fast_food_restaurant
  fertility
  financial_advising
  financial_service
  fish_farm
  fishmonger
  fitness_trainer
  flowers_and_gifts_shop
  food
  food_beverage_service_distribution
  food_delivery_service
  food_stand
  food_truck
  fountain
  freight_and_cargo_service
  fruits_and_vegetables
  furniture_store
  gas_station
  general_litigation
  go_kart_club
  graphic_designer
  grocery_store
  gym
  hair_salon
  halal_restaurant
  hardware_store
  health_and_medical
  health_food_store
  high_school
  hindu_temple
  history_museum
  holiday_rental_home
  home_cleaning
  home_developer
  home_goods_store
  home_improvement_store
  home_security
  home_service
  home_staging
  horseback_riding_service
  hospital
  hostel
  hotel
  hvac_services
  ice_cream_shop
  image_consultant
  immigration_law
  indian_restaurant
  indonesian_restaurant
  industrial_company
  industrial_equipment
  information_technology_company
  installment_loans
  insurance_agency
  interior_design
  internet_marketing_service
  internet_service_provider
  investing
  it_service_and_computer_repair
  italian_restaurant
  japanese_restaurant
  jewelry_store
  karaoke
  korean_restaurant
  laboratory_testing
  lake
  landmark_and_historical_building
  landscaping
  language_school
  laundromat
  lawyer
  legal_services
  library
  lighting_store
  lingerie_store
  lodge
  lounge
  luggage_store
  machine_shop
  makeup_artist
  marketing_agency
  martial_arts_club
  mass_media
  mattress_store
  media_news_company
  medical_center
  medical_research_and_development
  medical_service_organizations
  mens_clothing_store
  metal_fabricator
  metal_plating_service
  mexican_restaurant
  middle_school
  mobile_phone_store
  monument
  mosque
  motel
  motorcycle_dealer
  motorcycle_repair
  mountain
  movers
  movie_television_studio
  museum
  music_production
  music_school
  music_venue
  musical_instrument_store
  national_park
  naturopathic_holistic
  non_governmental_association
  nursery_and_gardening
  nutritionist
  obstetrician_and_gynecologist
  office_equipment
  optometrist
  oral_surgeon
  orthopedist
  outlet_store
  painting
  panamanian_restaurant
  park
  party_supply
  pawn_shop
  pentecostal_church
  pet_breeder
  pet_groomer
  pet_store
  pharmacy
  physical_therapy
  pilates_studio
  pizza_restaurant
  plastic_manufacturer
  plastic_surgeon
  plumbing
  political_organization
  pool_billiards
  powder_coating_service
  preschool
  print_media
  printing_services
  private_school
  professional_services
  professional_sports_team
  psychic
  psychologist
  psychotherapist
  pub
  public_and_government_association
  public_school
  public_service_and_government
  public_utility_company
  race_track
  radio_station
  real_estate
  real_estate_agent
  real_estate_service
  record_label
  religious_organization
  religious_school
  rental_service
  resort
  restaurant
  retail
  retirement_home
  river
  rv_park
  sandwich_shop
  school
  school_sports_team
  scuba_diving_center
  seafood_restaurant
  self_storage_facility
  senior_citizen_services
  sewing_and_alterations
  shaved_ice_shop
  shipping_center
  shoe_repair
  shoe_store
  shopping
  shopping_center
  sign_making
  smoothie_juice_bar
  soccer_field
  social_media_agency
  social_media_company
  social_service_organizations
  software_development
  souvenir_shop
  spas
  specialty_school
  sporting_goods
  sports_and_recreation_venue
  sports_club_and_league
  sports_wear
  stadium_arena
  steakhouse
  storage_facility
  structure_and_geography
  supermarket
  superstore
  surgical_appliances_and_supplies
  sushi_restaurant
  tattoo_and_piercing
  tea_room
  telecommunications_company
  test_preparation
  thai_restaurant
  theatre
  theme_restaurant
  thrift_store
  tire_dealer_and_repair
  topic_concert_venue
  topic_publisher
  tours
  town_hall
  toy_store
  trailer_dealer
  train_station
  transportation
  travel_company
  travel_services
  trophy_shop
  tutoring_center
  urban_farm
  urologist
  venue_and_event_space
  veterinarian
  video_game_store
  vietnamese_restaurant
  vitamins_and_supplements
  walk_in_clinic
  water_treatment_equipment_and_services
  wedding_planning
  weight_loss_center
  wholesale_store
  wine_bar
  winery
  womens_clothing_store
  yoga_studio

1:46

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

# Look at names
print('Sample names:')
for i, row in gdf.head(20).iterrows():
    names = row.get('names', {})
    if names and isinstance(names, dict):
        name_val = names.get('primary', '')
    else:
        name_val = str(names)
    print(f'  {name_val}')
" 2>&1

Sample names:
  Tincorp Pa TI
  Sara Couto - Intervenção Terapêutica
  แหลมใหญ่แล็บ คลินิกเทคนิคการแพทย์ สมุทรสงคราม
  Chittagong Victory National School-CVNS
  Quảng Cáo - In Nhanh SaPa
  Donata Farms & Land Services
  Vet Rx Pet Health Center
  Work N Wear International
  The Wagging Tail Salon
  Liso EstÚDio
  New Launch Property
  Ross Ice Shelf, Antarctica
  十刻刺青室
  Poeanuanua
  Discover Italy
  สอนต่อขนตาพัทยา ต่อขนตาพัทยา
  Krāslavas Varavīksnes vidusskola
  مركز هيا هير للشعر الطبيعي/  Haya Center for Natural Hair
  Esencia Alquímica: Aromaterapia y Cosmética Natural
  Maliha Farzam

2:16

I need to find research stations in Antarctica. The `place` type doesn't seem to have a specific "research station" category in the primary categories. Let me look at the `division` or `division_area` types, or maybe look at the `infrastructure` type. Also, let me check what water and land types look like.

Actually, for research stations, I might need to look at the `place` type more carefully for Antarctic research stations. Let me search for known Antarctic stations.

Let me also look at what categories might relate to research stations, and check if there's a specific Antarctica division.

2:16

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

# Look for things with 'research' or 'station' in names
import re
for i, row in gdf.iterrows():
    names = row.get('names', {})
    if names and isinstance(names, dict):
        name_val = names.get('primary', '')
        if name_val and ('research' in name_val.lower() or 'station' in name_val.lower() or 'base' in name_val.lower()):
            cats = row.get('categories', {})
            primary_cat = cats.get('primary', '') if isinstance(cats, dict) else ''
            print(f'{name_val} | category: {primary_cat}')
" 2>&1

Master Bees Stationeries & Souvenirs | category: souvenir_shop
Brown Station, Antarctic Peninsula | category: landmark_and_historical_building
Brown Station | category: landmark_and_historical_building
González Videla Antarctic Base | category: airport
Palmer Station, Antarctica | category: home_developer
New Lucena Municipal Police Station | category: public_service_and_government
Carlini Base | category: arts_and_entertainment
Bellingshausen Russian Antarctic Station | category: educational_research_institute
Base Marambio, Antartida Argentina | category: central_government_office
Marambio Base | category: airport
Base Antártica Marambio | category: public_and_government_association
Esperanza Base, Antarctica | category: landmark_and_historical_building
Base Esperanza, Antartida Argentina | category: airport
Neumayer-Station II | category: landmark_and_historical_building
Fuel Tech Micro Gas Station & Dispensers | category: industrial_equipment
Plateau Station | category: landmark_and_historical_building
Pioneer - Gas Station | category: gas_station
Base antarctique Mawson | category: landmark_and_historical_building
Antarctic Kunlun Station | category: landmark_and_historical_building
COFFE Station Old town | category: cafe
Base antártica Concordia | category: train_station
Base antarctique Concordia | category: landmark_and_historical_building
Base antarctique Dumont-d'Urville | category: landmark_and_historical_building
Mario-Zucchelli-Station | category: 
Base McMurdo | category: landmark_and_historical_building
Base antarctique McMurdo | category: landmark_and_historical_building
Lakeview Clinical Research | category: medical_research_and_development

2:38

Good, I can see several Antarctic research stations in the data. Let me look more systematically for places categorized as research stations or similar. Let me also look at the `division` type for boundaries and the `land` and `water` types.

For the research stations in Antarctica, I should look at the `place` type with specific categories. But first, let me think about the approach:

1. **Get research stations**: Use Overture `place` type south of -60 latitude, filtering for Antarctic research stations.
2. **Get landmass**: Use Overture `land` or `division` type for Antarctica's land.
3. **Get water features**: Use Overture `water` and `bathymetry` types.

Let me look at the categories more closely to find Antarctic research stations, and also check land and water data.

2:38

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

# Look specifically for Antarctic research stations
# Let's search for names containing antarctic, pole, south, etc.
import re
keywords = ['antarctic', 'research station', 'base', 'mcmurdo', 'amundsen', 'scott',
            'mawson', 'davis', 'casey', 'halley', 'rothera', 'san martin', 'progress',
            'novolazarevskaya', 'vostok', 'concordia', 'kunlun', 'troll', 'tor',
            'neumayer', 'kohnen', 'princess elisabeth', 'syowa', 'mizuho', 'asuka',
            'dumont', 'zheleznyak', 'mirny', 'bellinghausen', 'great wall', 'zhongshan',
            'taishan', 'jang bogo', 'sejong', 'king george', 'mario zucchelli',
            'vernadsky', 'akademik', 'polar', 'proposed', 'summer', 'winter',
            'fernando de noronha', 'trindade']

hits = []
for i, row in gdf.iterrows():
    names = row.get('names', {})
    if names and isinstance(names, dict):
        name_val = names.get('primary', '')
        if name_val:
            name_lower = name_val.lower()
            # Check if any keyword matches
            if any(kw.lower() in name_lower for kw in keywords):
                cats = row.get('categories', {})
                primary_cat = cats.get('primary', '') if isinstance(cats, dict) else ''
                hits.append((name_val, primary_cat, row['id']))

for name, cat, id_ in hits:
    print(f'{name} | {cat} | {id_}')
print(f'Total: {len(hits)}')
" 2>&1

Chittagong Victory National School-CVNS | school | 8444e6a7-a674-4e32-9bda-f3ea6ebb472a
Ross Ice Shelf, Antarctica | landmark_and_historical_building | b79d49c5-0a89-4ef8-a8da-bf68c3ec55f7
Suzuki Nilphamari - Lahin Motors | motorcycle_dealer | 4ab2ad89-dba7-4170-9b59-c5a1db7346bc
Discount Hardware Store | hardware_store | f6439189-50c5-4ca4-93ae-1c4fc23a0f80
Everything Store | shopping | 363e60c8-6cac-4c36-89eb-4e3c6ceec8fc
West Antarctica | national_park | 3d347cf1-2e41-4948-a3d3-e371f814c1b7
AKS Manufactory | engineering_services | cce28796-8e57-4908-a5ed-42c7a8ac0c97
In Aeternum Historia | print_media | b9f600a7-98dd-4d9b-b1a2-70d1d79ed9b2
Mitra Fajar Ciawi " Bengkel Resmi Sepeda Motor Honda " | motorcycle_repair | d56e2d32-9082-4811-9bb6-7435eb79f16d
Canoon Store | mobile_phone_store | 431e85c0-b192-4e9b-9982-3835d55123ed
Toronto Cosmetic Surgery Institute | hospital | a2d99f87-31ba-4559-9812-229f26ab3268
شركة يوني فليكس للمعدات والمستلزمات الطبية - Uniflex Medical Supply Store | pharmacy | 20c0a280-31c5-4e18-b438-95d16c3d0ae0
Antarctic Peninsula | landmark_and_historical_building | 369e9e42-ef14-4cb1-a615-7aa3df2ae812
ร้านข้าวแกงฮาริษา Harisa's Curries Rice Factory | restaurant | 273a56f8-acc7-4b8f-964c-79c35ac399b6
Mount Wilson (Antarctica) | mountain | 229a95d3-fdef-4f56-83c2-df08f47a6350
Lihor Store Fan | fruits_and_vegetables | 341ceb2b-9e20-4306-9014-6cd4840f73e2
Errera Channel, Antarctica | landmark_and_historical_building | fa14230c-0750-4534-b8cd-32b81c732fbb
Paradise Bay, Antarctic Peninsula | landmark_and_historical_building | 7efe1412-bd47-4dab-9014-91f8756d5bb6
Brown Station, Antarctic Peninsula | landmark_and_historical_building | ee1f6b90-83cf-4f5b-81ed-b11d5920732b
González Videla Antarctic Base | airport | ba9d8ef6-32f3-4098-a010-b5b485e4e75f
Port Lockroy, Antarctic Peninsula | landmark_and_historical_building | 2a100ade-be10-41c0-b6c2-a49e921a24e7
Mount William (Antarctica) | mountain | 95a94038-ab7f-456c-abde-8ebb5d675acd
Lemaire Channel, Antarctic Peninsula | landmark_and_historical_building | 7da59934-acaf-4bea-89c7-be05d8f48a42
Palmer Station, Antarctica | home_developer | f73848f3-abaa-432b-b3ed-8092a6b39b9e
Mount Jackson (Antarctica) | mountain | d65cdcdb-703f-48cb-af6d-fcb0838dd9f6
Esperanto Island, Antarctica | landmark_and_historical_building | 3bf8e968-6050-4ee6-8a60-f046db54cc02
Cierva Cove, Antarctic Peninsula | landmark_and_historical_building | 5c3dfca0-16e9-4630-96ac-8b603d195332
Antarctica, Antarctic Circle | landmark_and_historical_building | 9d364bf8-eaaf-4f79-ad45-195f74f49a93
Mikkelsen Harbour, Antarctic Peninsula | landmark_and_historical_building | e254628a-91d9-4e80-be28-87bd67775dbc
Carlini Base | arts_and_entertainment | c37b7fbf-894a-4c23-954e-6209aed96378
Bellingshausen Russian Antarctic Station | educational_research_institute | 6240b821-30cb-4fe4-a08a-6fddb4fe2978
Concretiza Lar Construtora E Incorporadora | home_developer | 845e2bea-5d5f-427e-8998-4c7e8df3a27d
King George Bay (Antarctica) | landmark_and_historical_building | 78c69e17-0ff6-4498-ba7f-84ddf5de0550
Base Marambio, Antartida Argentina | central_government_office | 18414d58-4e4c-480f-bfab-ee3e224c5b5e
Marambio Base | airport | 015df87e-1265-4d80-844b-97a4c1af44a5
Base Antártica Marambio | public_and_government_association | 11037d14-337b-4a53-a23b-85566484f016
Chapel of the Blessed Virgin of Lujan, Antarctica | church_cathedral | 4775fc70-d68b-48b4-af40-377af7e9a457
Esperanza Base, Antarctica | landmark_and_historical_building | 8536af5a-82ba-46ed-bd6c-069082cbfc5d
Base Esperanza, Antartida Argentina | airport | b1662153-d852-4a90-b8a7-5d5e113439bb
British Antarctic Territory | landmark_and_historical_building | 0f82cde8-1ca3-41ca-9c40-3b1778e62199
Jo Winterton Yoga & Sound | yoga_studio | 84829b84-dc3d-4dba-8c09-e5f83ba2cd48
Consultoría Psicológica Especializada de Monclova Psic. Aarón Hernández | psychologist | 81aa5033-c464-4230-9d5f-342f627a94a5
Generika Drugstore - ZDN Bagting Dapitan Page | pharmacy | 2292cc08-78f7-41ba-9dc3-595471d11ae1
Carrefour Hiper Recife Torre | supermarket | b2ebe4bf-d8af-4fa9-9b9d-d20b7fa5acfc
Pastor Jorge |  | 6c1b345f-ab13-4f59-afa5-559df042951c
Nails By Hilary Torres M | beauty_salon | c2a97ba3-c389-4a6f-8cc3-4cb2e7ba276a
Famous Forum of Worldwide Collaborators LLP | education | 60a78da1-25e2-44d8-ac77-a596fa24b7a7
Javastasia Distributors Halaal Meats | butcher_shop | 7a3e0fb0-261d-448d-8b62-1c586daf50fa
N9store.ro | beauty_salon | 0df9b89a-29b0-44e9-a84b-1f6fde43ee33
La Combo Tortuga |  | 686ff5e8-02d5-402a-85ce-8d136d91dbd9
Storybook Imaging | professional_services | d2b6014e-68f8-4a06-a739-bd1ffa4cb861
Technology Store | mobile_phone_store | ef9cba20-3a8b-4ba7-88ee-721a58ede118
Alessandra Oliveira corretora de imóveis rj | real_estate | 8c71f2a6-28d8-45f1-9690-cd671992d554
Robin Rolon, Mary Kay Independent Sales Director |  | 4ca42e95-5f32-4564-a61e-d22edad9f0e1
Vumeer el Mentor | professional_sports_team | 6dd0c797-dbd3-4222-8d54-11a17b8a28e8
Babul Yaman Store | shopping | cd40e85b-48bf-4a95-b777-2310ce51de03
Neumayer-Station II | landmark_and_historical_building | 0038d9b1-d883-46c9-b499-517825a4cd07
Reis Motors | motorcycle_repair | 124a7598-a6a0-4c77-88e1-840469315dcf
Troll Airfield | airport | 1a1f77c7-582d-4672-81bb-07dd1fcab9ce
Antarctica/Troll | landmark_and_historical_building | e3417e97-f283-4ecc-a32b-ae164e69903f
Grace-Care Laboratory Services and Supplies | laboratory_testing | de741fe0-3b27-459e-97f0-aa477515bf18
Yamu Store III | music_venue | 8e765024-1e06-46e2-ae68-530ac3997595
Jj'sanm Music Tutorial Center | music_school | 92ee6d8a-3b40-49c9-a5bd-5712c493b34c
Dani Motors | car_dealer | 5294e781-7d01-4a03-ae99-6a5ea95035ff
Delta Power Generators | energy_company | f3542fb3-b896-4edb-b776-cba290d7278b
Timsun Franchise Bayan Lepas - West Motor Racing Centre | motorcycle_repair | e2c6c29c-78fa-401a-bde2-66d2c083d3cc
Nadia Pereira - Corretora de Imóveis | brokers | 2dcc4cb1-fe8e-4b5f-b567-f13794c3ace1
RT Power Motor | machine_shop | bbc67af4-6ba3-41e9-b317-9d1b714c43be
King World Product Store | professional_services | 31f4d815-b16c-42bf-b64f-a260e1eed2ed
BOSS Menswear Store | clothing_store | 6f6d338c-6401-4067-bcb9-929793712e6e
Extra Space Storage | self_storage_facility | b48cdbff-6084-48ee-83c2-d879d677e13b
Extra Space Storage | self_storage_facility | 684fc7b4-bcf3-4331-96d7-49ef377eb50f
Ashford Self Storage Hull Ltd | storage_facility | c0c822bc-d3c0-4abb-838f-94c8e44940d1
HomeVestor PH | real_estate_service | 4421cbfa-913f-4dfd-9f8d-75864926748c
Tractor Zetor | agricultural_cooperatives | d304328a-7137-4457-bc91-c0e67f619317
Pizza Salvatoré | fast_food_restaurant | e53bb1a5-50a0-49bd-8e97-85431630c05a
Pizza Salvatoré | fast_food_restaurant | c3cb3d33-c050-4607-979e-9a7373b55118
Pizza Salvatoré | fast_food_restaurant | 846f5099-9179-4cb0-a260-2a294a042ae4
Pizza Salvatoré | fast_food_restaurant | 45f1fe4a-8416-4599-9899-675c1b75c659
Pizza Salvatoré | fast_food_restaurant | 4db3b07e-edaa-4008-8a9a-e1bfa5ec9564
Pizza Salvatoré | fast_food_restaurant | 659f2761-bf50-451e-9cb3-b77a888a20b0
Pizza Salvatoré | fast_food_restaurant | 10279c90-b532-426b-8c48-ae5c2d85db63
Pizza Salvatoré | fast_food_restaurant | 1a77c5fb-4cab-4dec-955e-30ecc678f6fe
Pizza Salvatoré | fast_food_restaurant | bd4f4168-db1a-40e7-bd78-cf0dae845d73
Pizza Salvatoré | fast_food_restaurant | 917936c5-4da0-4fba-8af3-afcb619e162f
Pizza Salvatoré | fast_food_restaurant | f5b3eed7-ad93-4191-bf98-4e98ba215f20
Pizza Salvatoré | fast_food_restaurant | 589ff74b-381b-4965-9559-44b6447b46d6
Pizza Salvatoré | fast_food_restaurant | ce7ec5c9-e6ec-465d-b76c-7d16b75b7533
Pizza Salvatoré | fast_food_restaurant | d8e80885-2ce1-4e8b-a378-593baac6fdd1
Pizza Salvatoré | fast_food_restaurant | c49b6f3f-29ac-420f-b94c-76c2927062be
Pizza Salvatoré | fast_food_restaurant | af2f9b1d-d401-44b0-ab07-2d3c56736776
Pizza Salvatoré | fast_food_restaurant | 0e807cf9-1fbe-4213-8172-fd6dae583aa7
Pizza Salvatoré | fast_food_restaurant | 6c0c5c4e-d101-4ef9-81c5-29f7294ec232
Pizza Salvatoré | fast_food_restaurant | 64002352-1e73-47f8-add3-7320faea0a41
Pizza Salvatoré | fast_food_restaurant | 3635d36a-c320-4f00-8933-ade9f9209261
Pizza Salvatoré | fast_food_restaurant | df57d88a-54b9-4528-814a-3242a8ee81d1
Pizza Salvatoré | fast_food_restaurant | 1eab355a-5a1c-4fca-9b5d-345c2ec900ba
Pizza Salvatoré | fast_food_restaurant | 47429912-b4f4-49cc-b5c8-4a532c4c9fc5
Pizza Salvatoré | fast_food_restaurant | a4358fc6-3af9-4a4d-9a6d-12100e2283df
Pizza Salvatoré | fast_food_restaurant | c507bdd2-48fe-4518-8544-61c490853615
Pizza Salvatoré | fast_food_restaurant | 1992f3dd-cd0a-463d-8f7e-985e9bda8837
Pizza Salvatoré | fast_food_restaurant | 00a21541-4dfd-4e8a-a4d0-9bca7349c3f7
Pizza Salvatoré | fast_food_restaurant | ec244d5f-e148-4b43-973a-ce47d02db493
Pizza Salvatoré | fast_food_restaurant | 4652810e-9426-4d62-85aa-f60eea5ec808
Pizza Salvatoré | fast_food_restaurant | 27068687-b24e-449b-a63a-c0a60e8c22ae
Pizza Salvatoré | fast_food_restaurant | a7c05f29-a890-4a53-bdbe-ca9c35f99fa0
Pizza Salvatoré | fast_food_restaurant | 299d6b7f-be15-4710-949b-e8c70efbd9b7
Pizza Salvatoré | fast_food_restaurant | c5508ee9-0a17-4efb-ade0-25b3576b8aab
Pizza Salvatoré | fast_food_restaurant | 1a176c1c-0a4b-4065-a580-b8f955cea263
Pizza Salvatoré | fast_food_restaurant | 5623057a-f422-4395-8b27-7cb06acfcf0f
Pizza Salvatoré | fast_food_restaurant | 2d66500e-8ce9-4f28-b02a-af576015e763
Pizza Salvatoré | fast_food_restaurant | ba90a2d0-6653-4913-846c-d06fa36e7b19
Pizza Salvatoré | fast_food_restaurant | 19b5f219-14e9-45d9-a54c-629afadf4e3a
Pizza Salvatoré | fast_food_restaurant | dbd62283-688b-49a1-be9b-2be92d4bb64a
Pizza Salvatoré | fast_food_restaurant | 5fc21298-b28a-4867-b4d0-6c391230db79
Pizza Salvatoré | fast_food_restaurant | eda6fc34-8893-4a13-8949-214938a08d02
Pizza Salvatoré | fast_food_restaurant | 13b850d5-f456-4e86-a04e-6905e03ec7a2
Pizza Salvatoré | fast_food_restaurant | ac0cceb0-514b-4db0-8292-47ed1cc09ab5
Pizza Salvatoré | fast_food_restaurant | 4c91fe5c-5922-41cd-a628-8b3cbd7573e4
Pizza Salvatoré | fast_food_restaurant | f08f075a-a98b-4636-8ef8-6d2650e78219
Pizza Salvatoré | fast_food_restaurant | 57adc64e-c8ff-4b6b-81ab-bec56fd46e1c
Pizza Salvatoré | fast_food_restaurant | 5dac445c-c99d-4fb2-af33-dd39e0eaabd2
Pizza Salvatoré | fast_food_restaurant | 9e079f28-7cd2-4cf8-85cb-e50a6a26961f
Pizza Salvatoré | fast_food_restaurant | 76c8c0a6-ba5c-4b2b-8de0-f5e631fd7e56
Pizza Salvatoré | fast_food_restaurant | 17cb7a06-f84f-433b-99f5-a4a53432d998
Pizza Salvatoré | fast_food_restaurant | 521e0e02-cc97-4a10-80a3-cbc517082362
Pizza Salvatoré | fast_food_restaurant | ff973830-cd8a-4a11-9a10-15c140509cfd
Pizza Salvatoré | fast_food_restaurant | f2f634bc-5d52-44b4-b830-a355ef8ea9cd
Pizza Salvatoré | fast_food_restaurant | 427bd142-82a3-4d80-af0f-1d045be9561c
Pizza Salvatoré | fast_food_restaurant | 6c5f1b16-bff3-42cf-be38-45d50798d70b
Pizza Salvatoré | fast_food_restaurant | e876aed9-acde-4cec-9e84-62317579fa23
Pizza Salvatoré | fast_food_restaurant | 0a304118-3be2-4228-bf14-d4adb5e677a4
Pizza Salvatoré | fast_food_restaurant | e818e6ff-480c-44eb-8d19-890d28d74af0
Pizza Salvatoré | fast_food_restaurant | d548562a-e090-482b-9421-384f06717bd5
Pizza Salvatoré | fast_food_restaurant | 51cfbabb-acc8-4042-85ae-8c66570d0448
Pizza Salvatoré | fast_food_restaurant | 919f9b9c-b383-4e34-a4ec-794f3c199ef4
Pizza Salvatoré | fast_food_restaurant | 313305e8-bca8-4b83-8cf6-0a209403b741
Pizza Salvatoré | fast_food_restaurant | fc92bade-916f-4530-9fc3-4047f607143d
Pizza Salvatoré | fast_food_restaurant | 563e8db3-fa2a-45aa-87bc-6f1a90724df0
Pizza Salvatoré | fast_food_restaurant | 6ee810b0-b1bd-41c8-9dde-e5a683ade3c5
Pizza Salvatoré | fast_food_restaurant | a06efdd3-7fb4-4bd8-adc8-a37bb0ae2a8f
Pizza Salvatoré | fast_food_restaurant | d063f7fc-e31e-4a17-aaad-8323ce0335aa
Pizza Salvatoré | fast_food_restaurant | 94716afd-9970-49d2-9a46-443df6511415
Pizza Salvatoré | fast_food_restaurant | 7fb9de40-7904-49ca-83bd-cdce6646a44d
Pizza Salvatoré | fast_food_restaurant | 132add6a-b878-41bf-8c27-e4538267e3f0
Lake Glubokoye (Antarctica) | lake | 37ec72ed-aef2-436d-ba26-70bbe6926458
Pizza Salvatoré | fast_food_restaurant | 4ffb7893-bead-4e40-b3c8-8a4219af3eac
Pizza Salvatoré | fast_food_restaurant | 6ebec723-13a9-4bfb-8d24-f6782f2d15ed
Pizza Salvatoré | fast_food_restaurant | 7935ee5f-cacc-47e2-9ec6-67009c73f0bd
Pizza Salvatoré | fast_food_restaurant | 4f4beacb-a69f-4a76-b15c-da5117e1bf69
Pizza Salvatoré | fast_food_restaurant | 2ab03936-be4e-4f92-ba03-0ade8bd645c3
Pizza Salvatoré | fast_food_restaurant | 24252973-73a8-4012-b4c1-d7d16be2241d
Pizza Salvatoré | fast_food_restaurant | f1247375-7c14-4807-ac01-b7e42021892f
Pizza Salvatoré | fast_food_restaurant | 85994c90-67cc-49ad-8a06-df82a3ef356d
Pizza Salvatoré | fast_food_restaurant | 3222e111-c4a2-45e0-8f54-12861fed5406
Pizza Salvatoré | fast_food_restaurant | 0f9c5cf3-0925-411b-a85e-f4311ae5ff73
Pizza Salvatoré | fast_food_restaurant | ddfc41af-f6ca-4bfc-8029-e66865409369
Pizza Salvatoré | fast_food_restaurant | ce818547-45cf-4f8f-a9d2-90d43c1f1a61
Pizza Salvatoré | fast_food_restaurant | 92d55e0f-5481-4112-bc90-aa03d913a9c4
Pizza Salvatoré | fast_food_restaurant | 94fa1b5c-2048-4ca3-9910-e711be9b0d5d
Pizza Salvatoré | fast_food_restaurant | cdeb88ff-33b2-40ce-9c5f-26777d5b91ac
Pizza Salvatoré | fast_food_restaurant | 53099e04-6a7f-48f8-bc45-f94a606b1fc5
Pizza Salvatoré | fast_food_restaurant | 6c2937e0-c828-4627-b4f3-14e306086ba6
Pizza Salvatoré | fast_food_restaurant | 1bb8bc82-bab5-4714-97ff-b2599b7ecaec
Pizza Salvatoré | fast_food_restaurant | fbfd4032-1fed-40a9-bd11-4eca3285a0eb
Pizza Salvatoré | fast_food_restaurant | d1060ffe-0a18-4a40-a25e-82c24327ea5b
Pizza Salvatoré | fast_food_restaurant | d4298951-af71-4797-b240-b210b5483df1
Pizza Salvatoré | fast_food_restaurant | 9cb6d44a-fa55-46ea-af8b-fd0b9942cf7d
Pizza Salvatoré | fast_food_restaurant | b1c4ba18-8fc2-4844-9cc5-fb4fb4e4baf1
Pizza Salvatoré | fast_food_restaurant | 5224beca-4d26-4c68-a4ba-43b594376a9e
Pizza Salvatoré | fast_food_restaurant | a2590a92-76c6-4387-9e57-9e641ff20e04
Pizza Salvatoré | fast_food_restaurant | de4d7b27-146f-4278-b094-d35e35064e40
Pizza Salvatoré | fast_food_restaurant | e7b373fe-6af9-4f94-b877-3f5cb1066205
Pizza Salvatoré | fast_food_restaurant | b8ad6605-8439-4873-bd8d-a92fb2d3b4fb
Pizza Salvatoré | fast_food_restaurant | 79e6db05-e3b6-43ad-84e5-65e327c3f44d
Pizza Salvatoré | fast_food_restaurant | 5d139ee2-c510-4729-92a7-1274cae59d89
Pizza Salvatoré | fast_food_restaurant | 6d67ae4b-9b55-4a7b-9ced-5f2d01252a4f
Pizza Salvatoré | fast_food_restaurant | 8dedc94b-812b-44e7-b989-5c2b1d895311
Pizza Salvatoré | fast_food_restaurant | 7a5eff53-1530-4704-921f-99fe00155b2f
Pizza Salvatoré | fast_food_restaurant | 925a972e-de25-4754-afd7-bd73a35063ad
Pizza Salvatoré | fast_food_restaurant | d51f7a02-8ef2-4891-9e7b-47488c2ef9a0
Pizza Salvatoré | fast_food_restaurant | 0c40be83-37c2-4a9f-9352-9b28ee30dd23
Pizza Salvatoré | fast_food_restaurant | bb40a8cd-d0b5-4a21-8631-70441c191617
Pizza Salvatoré | fast_food_restaurant | 5e4225db-a6b5-40b2-b402-33bb74a60fa8
Pizza Salvatoré | fast_food_restaurant | f054cbc5-7b87-40a5-a2ef-edcc8e7dafb9
Pizza Salvatoré | fast_food_restaurant | 005d770d-1e22-447f-9187-e597e46a1cf8
Pizza Salvatoré | fast_food_restaurant | fca37627-c23b-47bc-982e-60fac0b04e63
Pizza Salvatoré | fast_food_restaurant | 0e04ebc2-0320-40a5-8425-067a76cfa120
Pizza Salvatoré | fast_food_restaurant | a203de33-0286-4519-961e-97c589153279
Pizza Salvatoré | fast_food_restaurant | 06103274-839e-402e-bc2a-cbd07a2797c0
World Store | shopping_center | ed752a35-41e9-4031-8d17-7f660b7a6b15
Scott Mountains | mountain | 3a066dfa-16ff-425b-9a03-3d7b7f5f21d5
Pizza Salvatoré | fast_food_restaurant | abbec2d0-3574-4aa5-a963-997f8e0b69ed
Pizza Salvatoré | fast_food_restaurant | e1d977e7-c86c-4ea7-8b8e-89e0ca1a3c1c
Pizza Salvatoré | fast_food_restaurant | 33667367-aa10-4673-b897-a3ecf0fa11f7
Pizza Salvatoré | fast_food_restaurant | 1366c254-ab93-4e97-8dc5-8b7c2ffd84a7
Pizza Salvatoré | fast_food_restaurant | 2a9c8b43-ddc6-4a3e-a3b8-cfbd55ea956f
Pizza Salvatoré | fast_food_restaurant | 3c5a0204-d371-4539-8239-ddf065157ab6
Pizza Salvatoré | fast_food_restaurant | f9262548-0248-4922-b4d6-deed44103f29
Pizza Salvatoré | fast_food_restaurant | 2efbb32b-07d1-472a-8aff-13cd9d9c3f26
Beaver Lake (Antarctica) | landmark_and_historical_building | 234aa592-5b23-41d4-86a3-ef4aec4d6257
Ténor store | mobile_phone_store | 8ac92236-21d0-41df-9fa1-918e3ec63f69
Nissan Pampanga by Maica Torres | car_dealer | 0584bb7a-8130-43b6-87db-1dc84c3b2c96
Nicholas Range (Antarctic) | mountain | bf4691e5-cd16-4a40-844f-95574bbffe88
Forland KpK Motors | commercial_vehicle_dealer | 3083fd50-dd29-489c-bf07-02c8606f989b
Base antarctique Mawson | landmark_and_historical_building | 0f436ec9-9483-4218-848d-20b3a7dc3557
N'Zemmula - ristorante siciliano | italian_restaurant | d81b3a28-b3f3-4d59-856b-7330582d26d9
Nueva Ecija Doctors Hospital | hospital | e496e96f-b1ee-4c27-8ad2-0d97fdbdbe68
East Antarctica | landmark_and_historical_building | f5e5d673-82d4-4316-a017-75fef68c1fd6
Antarctic Kunlun Station | landmark_and_historical_building | 91c1becd-082b-4bda-85b3-c5e3a41e0a16
Lori Makiya, Realtor | real_estate_agent | 0ca1dbdc-51a0-476a-9e7b-2cdf2def24be
Melissa Isaac Realtor | real_estate_agent | ac0d6dc4-1a15-416f-be4e-85dbe712b4cd
Me_2 Store |  | 66b4bbf6-15b9-4dfe-b28c-d9569c97dba8
Victors motor driving school | driving_school | a427ee32-e010-410a-971a-741e19a34648
T-Store | mobile_phone_store | 05e52534-f12e-4eae-898b-161ab6927bc6
Lake Vostok | landmark_and_historical_building | 63b91770-28a5-4a5c-bea8-0e5842150734
Vostok İstasyonu | landmark_and_historical_building | bfd01a42-92a7-43f9-a7d3-0cc7fb3157b4
Bandith Card Store | wedding_planning | f8135242-0001-4175-9a25-e9a0ad91ff03
Base antártica Concordia | train_station | c5fd752b-15d7-46ec-ab3e-58bb4222ec5c
Base antarctique Concordia | landmark_and_historical_building | c0db4819-2dc3-4e47-826a-088864ba5e69
JRMSU Katipunan Campus CTEd-Laboratory High School | middle_school | 3f2577b8-4997-42de-b4fc-265ac932a346
Sunstar Motor Jepara | automotive_dealer | b283311b-22fa-420e-a1e8-beaa027ba33a
Good Mates Removals & Storage | movers | 7962b81f-ce90-4ece-a4d2-e8e339ec1a46
Kimstore | retail | b62b8f0a-8a79-49c4-9ae2-462cdb5a345c
Base antarctique Dumont-d'Urville | landmark_and_historical_building | 04d8258a-e765-4a4d-96c0-20cd3b11a9c3
Antarctic Plateau | landmark_and_historical_building | 26f0564a-e108-46da-b7aa-a4f3aca0daa2
Nadhi/நதி - The Online Saree Store |  | fc8b9976-1f7d-4d65-99db-602a36d6633e
North Store | shopping | ff0f0c95-5aa2-405a-afdf-b1d5b536d12b
Lake Bonney (Antarctica) | lake | 40060107-a899-46b3-8f95-ff266e9e7c24
McMurdo General Hospital | hospital | 82b40435-6b61-4d8e-9caa-7ff1d248b8a1
Base McMurdo | landmark_and_historical_building | d5882de0-07ff-46b9-a3c0-bbfb0cc9552c
Polární stanice McMurdo | landmark_and_historical_building | bb48b222-57c4-4c85-8743-752df4bba2ca
Base antarctique McMurdo | landmark_and_historical_building | d74e5a73-042c-4694-9e16-fa9622f203b3
Kimpich Store | beauty_and_spa | 10a87045-440c-48e0-8ba2-2134bd7083dd
Sales Store |  | 87138c83-09c1-4415-ade6-f94d851f9cea
Total: 235

3:19

The "place" theme isn't great for finding Antarctic research stations because it's a general POI dataset. Let me think about a better approach.

Actually, I think I should look at the "infrastructure" type in the "base" theme, or maybe look at different approaches. But the user specifically asked me to "pull stations from Overture." Let me look more carefully at the data.

Actually, let me reconsider. The "place" dataset does contain some Antarctic stations. But it's not comprehensive. Let me look more carefully at what's available and perhaps also check the "division" theme for possible station info.

Actually, Antarctic research stations are typically mapped as "places" in Overture Maps. Let me search more specifically in the place data.

Let me think about this differently. The user wants:
1. Antarctic research stations south of -60 latitude
2. Land and water features from Antarctica
3. 200 km operational radius for each station

For stations, I should look in the `place` type for research stations in Antarctica. The education_research_institute category seems promising. Let me also look more broadly.

Actually, let me just get all places south of -60 that have relevant names or categories, and supplement with well-known Antarctic stations.

But first, let me look at what's available more practically. Let me write a comprehensive script.

3:19

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

# Filter for educational_research_institute category
research = []
for i, row in gdf.iterrows():
    cats = row.get('categories', {})
    if cats and isinstance(cats, dict):
        primary = cats.get('primary', '')
        if primary == 'educational_research_institute':
            names = row.get('names', {})
            name_val = names.get('primary', '') if isinstance(names, dict) else ''
            research.append((name_val, row['id']))

for n, i in sorted(research):
    print(f'{n} | {i}')
" 2>&1

Bellingshausen Russian Antarctic Station | 6240b821-30cb-4fe4-a08a-6fddb4fe2978

3:58

The "places" dataset doesn't have a clean "research station" category. Let me look at this differently. Antarctic research stations are quite specific - I need to find them more systematically. Let me look at the Overture taxonomy more carefully, or use a different approach - maybe look for specific known stations.

Actually, let me think about the best approach. The user wants Antarctic research stations. Overture Maps has a "places" theme that includes POIs. But perhaps the `building` theme or something else might have them. Let me try a different approach - I'll query for stations more specifically.

Let me search for known Antarctic research stations by name. Known major stations include:
- McMurdo (US)
- Amundsen-Scott (US) - at South Pole
- Mawson (Australia)
- Davis (Australia)
- Casey (Australia)
- Dumont d'Urville (France)
- Concordia (France/Italy)
- Halley (UK)
- Rothera (UK)
- San Martín (Argentina)
- Marambio (Argentina)
- Esperanza (Argentina)
- Carlini (Argentina)
- Orcadas (Argentina)
- Bellingshausen (Russia)
- Novolazarevskaya (Russia)
- Vostok (Russia)
- Mirny (Russia)
- Progress (Russia)
- Neumayer (Germany)
- Troll (Norway)
- Tor (Norway)
- Syowa (Japan)
- Jang Bogo (South Korea)
- King Sejong (South Korea)
- Great Wall (China)
- Zhongshan (China)
- Kunlun (China)
- Taishan (China)
- Mario Zucchelli (Italy)
- Vernadsky (Ukraine)
- Bharati (India)
- Maitri (India)
- Scott Base (New Zealand)
- Gabriel de Castilla (Spain)
- Juan Carlos I (Spain)
- Arturo Prat (Chile)
- Bernardo O'Higgins (Chile)
- Eduardo Frei (Chile)
- Escudero (Chile)
- Palmer (US)
- Comandante Ferraz (Brazil)
- Wasa (Sweden)
- Svea (Sweden)
- Aboa (Finland)
- Arctowski (Poland)
- Henryk Arctowski (Poland)
- Macchu Picchu (Peru)
- Many others...

This is a lot. Overture may not have them all as "places". Let me look at what Antarctic stations are actually in the dataset by searching more carefully.

Actually, the user said "Antarctic research stations" - I should find whatever research stations I can from Overture. Let me look at the data more carefully.

3:58

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

# Let's look at all entries south of -60
# Look for any scientific/station related categories
sci_cats = set()
for i, row in gdf.iterrows():
    cats = row.get('categories', {})
    if cats and isinstance(cats, dict):
        primary = cats.get('primary', '')
        if primary and ('research' in primary.lower() or 'science' in primary.lower() or 'station' in primary.lower() or 'base' in primary.lower() or 'government' in primary.lower() or 'educational' in primary.lower()):
            sci_cats.add(primary)

print('Relevant categories:', sorted(sci_cats))
" 2>&1

Relevant categories: ['b2b_science_and_technology', 'central_government_office', 'educational_camp', 'educational_research_institute', 'educational_services', 'gas_station', 'medical_research_and_development', 'non_governmental_association', 'public_and_government_association', 'public_service_and_government', 'radio_station', 'train_station']

4:26

Let me think about this differently. Overture Maps places data from the `place` theme includes points of interest but the research stations in Antarctica are likely in the `building` theme or the `place` theme with the right categories. Let me look for stations more systematically - I should look at the actual geometry coordinates and find places that are south of -60 and are clearly research stations.

Actually, let me look at what we have more carefully. We found things like:
- Bellingshausen Russian Antarctic Station (educational_research_institute)
- Base McMurdo (landmark_and_historical_building)
- Various stations as landmark_and_historical_building
- Carlini Base (arts_and_entertainment)
- etc.

But the `place` theme has only 2348 rows for the entire area south of -60. Maybe I should also look at the `building` theme, or just use the `place` theme with broader filtering.

Actually, let me reconsider. Research stations in Overture might be in the `infrastructure` theme or just as places. Let me look at what's available and build a more comprehensive approach. I think the best approach is to:

1. Get all `place` features south of -60 
2. Filter for research stations (those with names containing station, base, research etc. and relevant categories)
3. Also check the `building` theme for potential stations
4. Get `land` theme for Antarctic landmass
5. Get `water` and `bathymetry` themes for water features

Let me also look at the infrastructure theme which might have research stations.

4:26

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)

# Check infrastructure
gdf = geodataframe('infrastructure', bbox=bbox, stac=True)
print('Infrastructure columns:', list(gdf.columns))
print('Infrastructure shape:', gdf.shape)
if len(gdf) > 0:
    print('Sample:', gdf.head(3))
" 2>&1

Infrastructure columns: ['id', 'geometry', 'sources', 'names', 'level', 'wikidata', 'source_tags', 'subtype', 'class', 'height', 'surface', 'version', 'bbox']
Infrastructure shape: (944, 13)
Sample:                                      id  ...                                               bbox
0  e9e3d506-89c0-3473-8cee-5e5ac6596d6c  ...  {'xmin': -180.0, 'xmax': -179.99998474121094, ...
1  47ca9240-243e-395c-8622-fb01f770a2af  ...  {'xmin': -167.39999389648438, 'xmax': -167.399...
2  32fccda2-4de8-3cc9-bba6-77d1ba40c1a9  ...  {'xmin': -157.33090209960938, 'xmax': -157.330...

[3 rows x 13 columns]

5:04

Let me look at what's in the `place` dataset more carefully for Antarctic research stations. The Overture `place` dataset seems to have a lot of irrelevant data that's just categorized incorrectly. Let me check the division_area and other datasets for research stations too.

Actually, the best source for Antarctic research stations might be the Overture `division` themed data (like division_area with locality type), or we could use OSM data with osmium. Let me also check what's available in `division_area` for Antarctica.

But wait - the user says "pull stations from Overture". Let me look more carefully at the place data for things that are actual Antarctic research stations. I'll look at the coordinates to see which ones are actually in Antarctica (south of -60).

5:04

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

# Filter by those whose names contain recognizable antarctic station names
# Known Antarctic research stations
known_stations = [
    'mcmurdo', 'amundsen-scott', 'scott base', 'mawson', 'davis', 'casey',
    'halley', 'rothera', 'san martin', 'progress', 'novolazarevskaya', 
    'vostok', 'concordia', 'kunlun', 'troll', 'tor', 'neumayer', 'kohnen',
    'princess elisabeth', 'syowa', 'mizuho', 'asuka', 'dumont',
    'mirny', 'bellingshausen', 'great wall', 'zhongshan', 'taishan',
    'jang bogo', 'sejong', 'mario zucchelli', 'vernadsky', 'carlini',
    'esperanza', 'marambio', 'orcad', 'brown', 'palmer', 'macquarie',
    'molodezhnaya', 'leningradskaya', 'russian', 'south pole',
    'plateau station', 'byrd', 'sledge', 'eights', 'brockton',
    'berkner island', 'general belgrano', 'belgrano', 'petrel',
    'decepcion', 'matienzo', 'primavera', 'cámara', 'melchior',
    'teniente', 'frei', 'escudero', 'artigas', 'comandante ferraz',
    'ohiggins', 'gabriel de castilla', 'juan carlos i',
    'king sejong', 'antártica', 'antarctica'
]

hits = []
for i, row in gdf.iterrows():
    names = row.get('names', {})
    if names and isinstance(names, dict):
        name_val = names.get('primary', '')
        if name_val:
            name_lower = name_val.lower()
            if any(kw in name_lower for kw in known_stations):
                cats = row.get('categories', {})
                primary = cats.get('primary', '') if isinstance(cats, dict) else ''
                hits.append((name_val, primary, row['id'], row.geometry.y, row.geometry.x))

print(f'{len(hits)} hits:')
for n, c, i, lat, lon in sorted(hits, key=lambda x: x[3]):
    print(f'  {n:50s} | {c:40s} | lat={lat:.4f}, lon={lon:.4f}')
" 2>&1

228 hits:
  BOSS Menswear Store                                | clothing_store                           | lat=-86.8161, lon=36.1071
  Yamu Store III                                     | music_venue                              | lat=-84.5414, lon=1.4062
  In Aeternum Historia                               | print_media                              | lat=-84.1257, lon=-94.9229
  HomeVestor PH                                      | real_estate_service                      | lat=-82.7436, lon=43.0545
  Consultoría Psicológica Especializada de Monclova Psic. Aarón Hernández | psychologist                             | lat=-82.5591, lon=-40.4187
  Babul Yaman Store                                  | shopping                                 | lat=-82.0149, lon=-17.7061
  Nueva Ecija Doctors Hospital                       | hospital                                 | lat=-81.4142, lon=73.1013
  Antarctic Kunlun Station                           | landmark_and_historical_building         | lat=-80.4169, lon=77.1161
  Chittagong Victory National School-CVNS            | school                                   | lat=-80.4166, lon=-175.3833
  Carrefour Hiper Recife Torre                       | supermarket                              | lat=-80.4079, lon=-34.9103
  Nadhi/நதி - The Online Saree Store                 |                                          | lat=-80.2502, lon=151.8750
  East Antarctica                                    | landmark_and_historical_building         | lat=-80.0000, lon=80.0000
  Toronto Cosmetic Surgery Institute                 | hospital                                 | lat=-79.3815, lon=-79.3815
  Plateau Station                                    | landmark_and_historical_building         | lat=-79.2500, lon=40.5000
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-79.2000, lon=48.8055
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-79.0067, lon=48.2268
  West Antarctica                                    | national_park                            | lat=-79.0000, lon=-100.0000
  Vostok İstasyonu                                   | landmark_and_historical_building         | lat=-78.4644, lon=106.8373
  Grace-Care Laboratory Services and Supplies        | laboratory_testing                       | lat=-78.4089, lon=-0.4839
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-78.1316, lon=48.5721
  T-Store                                            | mobile_phone_store                       | lat=-78.0165, lon=98.7891
  Base McMurdo                                       | landmark_and_historical_building         | lat=-77.8500, lon=166.6667
  Polární stanice McMurdo                            | landmark_and_historical_building         | lat=-77.8500, lon=166.6667
  Base antarctique McMurdo                           | landmark_and_historical_building         | lat=-77.8500, lon=166.6667
  McMurdo General Hospital                           | hospital                                 | lat=-77.8468, lon=166.6693
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-77.8300, lon=48.0982
  Timsun Franchise Bayan Lepas - West Motor Racing Centre | motorcycle_repair                        | lat=-77.7784, lon=17.3689
  Lake Bonney (Antarctica)                           | lake                                     | lat=-77.7167, lon=162.4167
  Generika Drugstore - ZDN Bagting Dapitan Page      | pharmacy                                 | lat=-77.6177, lon=-42.7094
  Lake Vostok                                        | landmark_and_historical_building         | lat=-77.5000, lon=106.0000
  Extra Space Storage                                | self_storage_facility                    | lat=-77.4239, lon=38.9075
  Extra Space Storage                                | self_storage_facility                    | lat=-77.0941, lon=38.8842
  Ashford Self Storage Hull Ltd                      | storage_facility                         | lat=-76.8408, lon=40.7812
  Mitra Fajar Ciawi " Bengkel Resmi Sepeda Motor Honda " | motorcycle_repair                        | lat=-76.1849, lon=-87.6522
  Tractor Zetor                                      | agricultural_cooperatives                | lat=-76.0161, lon=50.6250
  Everything Store                                   | shopping                                 | lat=-75.8400, lon=-100.1338
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-75.8088, lon=45.4189
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-75.6745, lon=45.4896
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-75.5072, lon=46.5485
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-75.4362, lon=45.5534
  Base antártica Concordia                           | train_station                            | lat=-75.1000, lon=123.3333
  Base antarctique Concordia                         | landmark_and_historical_building         | lat=-75.1000, lon=123.5580
  Windfreight Express Total Logistics Inc.(WETLI)    | freight_and_cargo_service                | lat=-75.0730, lon=43.0664
  Reis Motors                                        | motorcycle_repair                        | lat=-75.0369, lon=0.5273
  British Antarctic Territory                        | landmark_and_historical_building         | lat=-75.0000, lon=-50.0000
  Discount Hardware Store                            | hardware_store                           | lat=-74.9594, lon=-118.8281
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-74.6051, lon=45.6107
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-74.3313, lon=45.6563
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-74.2761, lon=46.0454
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-74.2186, lon=45.2646
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-74.1489, lon=45.8933
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-74.1314, lon=45.9495
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-74.1208, lon=45.2724
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-74.0916, lon=45.7029
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-74.0683, lon=45.8554
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-74.0208, lon=45.3783
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-74.0090, lon=45.7961
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.9428, lon=45.5280
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.9336, lon=45.6959
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.8763, lon=45.5761
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.8604, lon=45.3081
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.8508, lon=45.4915
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.8278, lon=45.6396
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.8122, lon=45.7659
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.7963, lon=45.4849
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.7572, lon=45.6672
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.7354, lon=45.6107
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.7027, lon=46.0356
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.6998, lon=45.5776
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.6991, lon=45.5384
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.6569, lon=45.7189
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.6117, lon=45.2670
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.6100, lon=45.7429
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.5726, lon=45.5839
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.5667, lon=45.3890
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.4862, lon=45.4996
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.4743, lon=45.4091
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.4692, lon=45.7536
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.4462, lon=46.0395
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.4408, lon=45.5120
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.4357, lon=45.5904
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.4314, lon=45.6865
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.4313, lon=45.4770
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.4255, lon=45.7589
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.3261, lon=45.5843
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.2982, lon=45.8941
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.2888, lon=45.5295
  Pastor Jorge                                       |                                          | lat=-73.2722, lon=-21.4893
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.2695, lon=45.3381
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.2131, lon=45.5555
  Sunstar Motor Jepara                               | automotive_dealer                        | lat=-73.1861, lon=127.6172
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.1016, lon=46.0413
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-73.0165, lon=45.4149
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.9898, lon=45.2866
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.9588, lon=45.6230
  Kimpich Store                                      | beauty_and_spa                           | lat=-72.9420, lon=170.5701
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.7842, lon=47.4376
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.7533, lon=45.4138
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.7474, lon=45.2019
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.7469, lon=46.5501
  Bandith Card Store                                 | wedding_planning                         | lat=-72.6176, lon=112.5000
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.5942, lon=46.3628
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.5897, lon=46.3360
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.5707, lon=45.6459
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.5557, lon=46.3708
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.5109, lon=46.2766
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.5046, lon=45.8880
  Forland KpK Motors                                 | commercial_vehicle_dealer                | lat=-72.3400, lon=57.9277
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.2313, lon=48.8805
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.2305, lon=48.5234
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-72.1301, lon=45.2762
  Liljequist Heights                                 | mountain                                 | lat=-72.1000, lon=-2.8000
  Antarctica/Troll                                   | landmark_and_historical_building         | lat=-72.0114, lon=2.5350
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.9961, lon=45.3674
  Troll Airfield                                     | airport                                  | lat=-71.9572, lon=2.4667
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.9477, lon=46.0572
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.9407, lon=45.3974
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.9283, lon=45.7712
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.8926, lon=45.3762
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.8692, lon=45.4083
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.8337, lon=46.8894
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.8025, lon=45.1337
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.7680, lon=46.2154
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.7272, lon=46.6816
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.6944, lon=46.7486
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.6445, lon=48.5450
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.6367, lon=46.5375
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.4401, lon=46.7483
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.4104, lon=46.8581
  Mount Jackson (Antarctica)                         | mountain                                 | lat=-71.3641, lon=-63.4122
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.3511, lon=46.7941
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.3342, lon=46.9120
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.3149, lon=46.1018
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.3001, lon=46.7188
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.2820, lon=46.7856
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.2784, lon=46.8329
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.2769, lon=46.8705
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.2455, lon=46.8382
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.2324, lon=46.8116
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.2184, lon=46.8125
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.2128, lon=48.4095
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.2086, lon=46.7308
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.1967, lon=46.8749
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.1907, lon=46.8988
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.1489, lon=46.8155
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.0943, lon=48.4517
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.0644, lon=48.3983
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-71.0284, lon=46.4482
  Canoon Store                                       | mobile_phone_store                       | lat=-71.0228, lon=-81.8109
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-70.9772, lon=46.6324
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-70.9488, lon=47.0135
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-70.9033, lon=45.5898
  Beaver Lake (Antarctica)                           | landmark_and_historical_building         | lat=-70.7833, lon=68.3000
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-70.7747, lon=46.2106
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-70.6936, lon=46.1303
  Neumayer-Station II                                | landmark_and_historical_building         | lat=-70.6500, lon=-8.2500
  Ross Ice Shelf, Antarctica                         | landmark_and_historical_building         | lat=-70.6126, lon=-177.8906
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-70.5664, lon=46.9811
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-70.5161, lon=47.4431
  Jj'sanm Music Tutorial Center                      | music_school                             | lat=-70.0807, lon=18.5354
  North Store                                        | shopping                                 | lat=-70.0807, lon=164.1299
  ร้านข้าวแกงฮาริษา Harisa's Curries Rice Factory    | restaurant                               | lat=-70.0806, lon=-65.2176
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-70.0413, lon=47.3714
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-69.5480, lon=47.8267
  JRMSU Katipunan Campus CTEd-Laboratory High School | middle_school                            | lat=-69.1798, lon=123.5915
  Nails By Hilary Torres M                           | beauty_salon                             | lat=-69.0986, lon=-28.9251
  Kimstore                                           | retail                                   | lat=-68.9110, lon=118.8281
  N'Zemmula - ristorante siciliano                   | italian_restaurant                       | lat=-68.8873, lon=66.4453
  Famous Forum of Worldwide Collaborators LLP        | education                                | lat=-68.5652, lon=-26.4430
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-68.5012, lon=48.4561
  Mount Wilson (Antarctica)                          | mountain                                 | lat=-68.4500, lon=-65.5500
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-68.2562, lon=49.2003
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-68.2076, lon=48.5943
  Victors motor driving school                       | driving_school                           | lat=-67.8553, lon=98.5670
  Lake Glubokoye (Antarctica)                        | lake                                     | lat=-67.6667, lon=45.8667
  RT Power Motor                                     | machine_shop                             | lat=-67.6134, lon=29.8849
  Base antarctique Mawson                            | landmark_and_historical_building         | lat=-67.6026, lon=62.8736
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-67.5111, lon=48.8527
  World Store                                        | shopping_center                          | lat=-67.4755, lon=52.8013
  Vumeer el Mentor                                   | professional_sports_team                 | lat=-67.2195, lon=-13.8102
  Base antarctique Dumont-d'Urville                  | landmark_and_historical_building         | lat=-66.6628, lon=140.0013
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-66.6472, lon=45.9763
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-66.3819, lon=50.2175
  Lihor Store Fan                                    | fruits_and_vegetables                    | lat=-66.3728, lon=-62.0934
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-66.0155, lon=45.3093
  Suzuki Nilphamari - Lahin Motors                   | motorcycle_dealer                        | lat=-65.7744, lon=-121.3709
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-65.6829, lon=47.6410
  Storybook Imaging                                  | professional_services                    | lat=-65.6583, lon=-5.6250
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-65.4902, lon=47.0309
  شركة يوني فليكس للمعدات والمستلزمات الطبية - Uniflex Medical Supply Store | pharmacy                                 | lat=-65.2936, lon=-75.2316
  Nadia Pereira - Corretora de Imóveis               | brokers                                  | lat=-64.9731, lon=27.5568
  Robin Rolon, Mary Kay Independent Sales Director   |                                          | lat=-64.9036, lon=-16.0130
  Brown Station                                      | landmark_and_historical_building         | lat=-64.8950, lon=-62.8700
  Brown Station, Antarctic Peninsula                 | landmark_and_historical_building         | lat=-64.8921, lon=-62.8724
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-64.8356, lon=46.1141
  Mount William (Antarctica)                         | mountain                                 | lat=-64.7833, lon=-63.6833
  Palmer Station, Antarctica                         | home_developer                           | lat=-64.7743, lon=-64.0531
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-64.7342, lon=46.0865
  Errera Channel, Antarctica                         | landmark_and_historical_building         | lat=-64.6965, lon=-62.5853
  La Combo Tortuga                                   |                                          | lat=-64.4728, lon=-40.7812
  Base Marambio, Antartida Argentina                 | central_government_office                | lat=-64.2413, lon=-56.6198
  Marambio Base                                      | airport                                  | lat=-64.2410, lon=-56.6268
  Chapel of the Blessed Virgin of Lujan, Antarctica  | church_cathedral                         | lat=-64.2407, lon=-56.6284
  Melissa Isaac Realtor                              | real_estate_agent                        | lat=-64.2389, lon=83.2983
  Base Antártica Marambio                            | public_and_government_association        | lat=-64.2374, lon=-56.6235
  Delta Power Generators                             | energy_company                           | lat=-64.0776, lon=11.7967
  Antarctica, Antarctic Circle                       | landmark_and_historical_building         | lat=-64.0536, lon=-60.9322
  Nissan Pampanga by Maica Torres                    | car_dealer                               | lat=-63.8013, lon=57.1124
  Pizza Salvatoré                                    | fast_food_restaurant                     | lat=-63.6763, lon=44.7646
  Good Mates Removals & Storage                      | movers                                   | lat=-63.4702, lon=123.9815
  N9store.ro                                         | beauty_salon                             | lat=-63.4702, lon=-42.5968
  Base Esperanza, Antartida Argentina                | airport                                  | lat=-63.3980, lon=-56.9974
  Esperanza Base, Antarctica                         | landmark_and_historical_building         | lat=-63.3977, lon=-56.9961
  Lori Makiya, Realtor                               | real_estate_agent                        | lat=-63.1742, lon=80.5643
  Concretiza Lar Construtora E Incorporadora         | home_developer                           | lat=-63.0673, lon=-59.9706
  Esperanto Island, Antarctica                       | landmark_and_historical_building         | lat=-62.6414, lon=-60.3647
  Me_2 Store                                         |                                          | lat=-62.3139, lon=88.2532
  Carlini Base                                       | arts_and_entertainment                   | lat=-62.2379, lon=-58.6668
  Bellingshausen Russian Antarctic Station           | educational_research_institute           | lat=-62.1998, lon=-58.9624
  King George Bay (Antarctica)                       | landmark_and_historical_building         | lat=-62.1000, lon=-58.0833
  Sales Store                                        |                                          | lat=-61.8769, lon=169.8047
  Ténor store                                        | mobile_phone_store                       | lat=-61.8563, lon=66.8828
  Javastasia Distributors Halaal Meats               | butcher_shop                             | lat=-61.6064, lon=-30.9375
  AKS Manufactory                                    | engineering_services                     | lat=-61.5202, lon=-97.7976
  Alessandra Oliveira corretora de imóveis rj        | real_estate                              | lat=-61.4396, lon=-18.8793
  Technology Store                                   | mobile_phone_store                       | lat=-61.2725, lon=-11.2557
  King World Product Store                           | professional_services                    | lat=-60.4919, lon=29.7141
  Dani Motors                                        | car_dealer                               | lat=-60.0484, lon=14.4141

5:46

The place data is quite noisy - lots of false positives from the keyword matching. But I can see some genuine Antarctic research stations. Let me think about the best approach.

The issue is that Overture Maps' `place` dataset has a lot of noise. Let me look at what kinds of places are truly in Antarctica (south of -60). Let me look at the data more carefully.

Actually, looking at the data, there are a LOT of "Pizza Salvatoré" entries in Antarctica which is clearly wrong data. These seem to be noisy/fake coordinates. Also many of the coordinates don't even make sense for Antarctica.

Let me think about a different approach. Maybe I should use `division` or `division_area` to get the Antarctic landmass. And for stations, let me look at the infrastructure types or use a different approach.

Actually, looking at this more carefully, the stations I can identify include:
- Bellingshausen Russian Antarctic Station (lat=-62.1998, lon=-58.9624)
- Carlini Base (lat=-62.2379, lon=-58.6668)
- McMurdo (lat=-77.85, lon=166.67)
- Antarctic Kunlun Station (lat=-80.4169, lon=77.1161)
- Base antarctique Concordia (lat=-75.1, lon=123.3)
- Base antarctique Dumont-d'Urville (lat=-66.66, lon=140.0)
- Neumayer-Station II (lat=-70.65, lon=-8.25)
- Troll (-72.01, 2.54)
- Base antarctique Mawson (lat=-67.60, lon=62.87)
- Marambio Base (lat=-64.24, lon=-56.63)
- Esperanza Base (lat=-63.40, lon=-56.997)
- Brown Station (lat=-64.89, lon=-62.87)
- Palmer Station (lat=-64.77, lon=-64.05)
- Vostok (lat=-78.46, lon=106.84)
- Plateau Station (lat=-79.25, lon=40.5)
- Base antarctique McMurdo (lat=-77.85, lon=166.67)

But these are all coming from "place" which is basically a POI dataset, not necessarily authoritative for research stations.

Let me try a different approach. Let me see if there's a better way to get Antarctic research stations. Perhaps I should look at infrastructure data, OSM data using osmnx or osmium, or try a different approach.

Actually, let me look at what the `division` theme has - it has division_area and division_boundary which might have Antarctic territorial claims or boundaries.

But actually, the most reliable approach for Antarctic research stations would be to use a known dataset. Let me check if there's a way to get them from OSM since osmnx is available.

Or better yet, let me look at what Overture `places` data is available with the category 'educational_research_institute' - we found only Bellingshausen. Let me search more broadly.

Actually, let me use a more targeted approach. I'll download the whole place dataset for south of -60, filter more carefully, and combine with known Antarctic station coordinates.

Wait, actually - the user just said "pull stations from Overture south of -60 latitude". So let me use the places data. The issue is that many places are tagged incorrectly in the area. Let me try to filter for places that are genuinely in Antarctica.

Looking at the data more carefully, there are lots of clearly wrong coordinates (Pizza Salvatoré stores showing up in Antarctica with coordinates like lat=-86... that's wrong). These are likely corrupt data or test data.

Let me take a different approach. Let me check if we can get research stations from OSM data using osmnx, or better yet let me see if the Overture `infrastructure` theme has what we need.

Actually, let me try an approach that's more practical. Let me:
1. Look at what Overture data identifies Antarctic stations
2. Use the ones that are clearly identifiable

Let me check Overture's place data more carefully, looking at what's actually in the Antarctic region with coordinates that make sense.

5:46

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

# Filter to only places with valid antarctic coordinates (south of -60, reasonable lon)
# And look at the names column for station-related names
import json

# Look at all places with geometry south of -60
real_antarctic = []
for i, row in gdf.iterrows():
    lat = row.geometry.y
    lon = row.geometry.x
    if lat < -60:  # Actually south of -60 deg
        names = row.get('names', {})
        name_val = names.get('primary', '') if isinstance(names, dict) else ''
        cats = row.get('categories', {})
        primary_cat = cats.get('primary', '') if isinstance(cats, dict) else ''
        real_antarctic.append((name_val, primary_cat, lat, lon, row['id']))

# Deduplicate by name
seen_names = set()
for n, c, lat, lon, id_ in sorted(real_antarctic, key=lambda x: x[2]):
    if n not in seen_names:
        seen_names.add(n)
        print(f'{n:60s} | lat={lat:8.4f} lon={lon:9.4f} | cat={c}')
" 2>&1

Claire's                                                     | lat=-89.9130 lon=  35.1168 | cat=fashion_accessories_store
Cobblestone Hotel & Suites - Wisconsin Rapids                | lat=-89.8179 lon=  44.3677 | cat=resort
Cobblestone Inn & Suites - Merrill                           | lat=-89.6556 lon=  45.1774 | cat=resort
Wissota Chophouse - Stevens Point                            | lat=-89.5834 lon=  44.5243 | cat=beer_bar
Cobblestone Hotel & Suites - Stevens Point                   | lat=-89.5834 lon=  44.5243 | cat=resort
Rhinelander                                                  | lat=-89.3829 lon=  45.6513 | cat=sandwich_shop
Boarders Inn & Suites by Cobblestone Hotels - Evansville     | lat=-89.2724 lon=  42.7797 | cat=
Boarders Inn & Suites by Cobblestone Hotels - Wautoma        | lat=-89.2673 lon=  44.0693 | cat=resort
Boarders Inn & Suites by Cobblestone Hotels - Columbus       | lat=-89.0295 lon=  43.3474 | cat=resort
Boarders Inn & Suites by Cobblestone Hotels - Ripon          | lat=-88.8679 lon=  43.8562 | cat=resort
Cobblestone Inn & Suites - Clintonville                      | lat=-88.7723 lon=  44.6190 | cat=resort
Cobblestone Inn & Suites - Altamont                          | lat=-88.7498 lon=  39.0453 | cat=resort
Boarders Inn & Suites by Cobblestone Hotels - Shawano        | lat=-88.6132 lon=  44.7563 | cat=resort
Cobblestone Hotel & Suites - Pulaski                         | lat=-88.2226 lon=  44.6531 | cat=resort
Cobblestone Hotel & Suites - Newton, IL                      | lat=-88.1862 lon=  38.9902 | cat=resort
Cobblestone Hotel & Suites - Paxton                          | lat=-88.1175 lon=  40.4552 | cat=resort
Cobblestone Inn & Suites - Brillion                          | lat=-88.0823 lon=  44.1846 | cat=resort
CORA Physical Therapy Hales Corners                          | lat=-88.0494 lon= -88.0494 | cat=physical_therapy
Hyatt House Chicago/Evanston                                 | lat=-87.6795 lon=  42.0455 | cat=venue_and_event_space
Santa Lolla                                                  | lat=-87.4805 lon= -63.8758 | cat=shoe_store
Boarders Inn & Suites by Cobblestone Hotels - Ashland City   | lat=-87.0765 lon=  36.2846 | cat=resort
Boss Outlet                                                  | lat=-86.8602 lon=  20.9849 | cat=clothing_store
BOSS Menswear Store                                          | lat=-86.8161 lon=  36.1071 | cat=clothing_store
Boarders Inn & Suites by Cobblestone Hotels Munising         | lat=-86.6216 lon=  46.3816 | cat=
Cobblestone Hotel & Suites - Salem                           | lat=-86.0895 lon=  38.6119 | cat=resort
Cobblestone Hotel & Suites - Charlestown                     | lat=-85.6548 lon=  38.4412 | cat=resort
Noble Roman's Pizzeria                                       | lat=-85.1120 lon=  35.3760 | cat=
Archives d'Orléans                                           | lat=-85.0000 lon=-116.9008 | cat=printing_services
Салон красоты Имидж                                          | lat=-84.9970 lon=   0.6462 | cat=beauty_salon
Ventura Auto Peças Ltda                                      | lat=-84.9968 lon= 131.9097 | cat=automotive_parts_and_accessories
足立屋                                                          | lat=-84.9905 lon=-107.5818 | cat=shopping
Modern American Schools of Egypt 2000                        | lat=-84.9901 lon= -36.5625 | cat=elementary_school
Jyomogang International Academy                              | lat=-84.9901 lon=  66.7969 | cat=educational_services
Ramaiah Polytechnic                                          | lat=-84.9865 lon= 104.9312 | cat=college_university
อึ้งกุ่ยเฮง มอเตอร์ไซค์ ฮอนด้า ยามาฮ่า รถมือสอง ร้อยเอ็ด     | lat=-84.9799 lon=  72.8648 | cat=motorcycle_dealer
Hilario                                                      | lat=-84.9781 lon=  47.4609 | cat=home_goods_store
One World Immigration Karachi                                | lat=-84.9709 lon= 168.3984 | cat=immigration_law
Dj daya                                                      | lat=-84.9283 lon= -62.8125 | cat=music_production
แต้ย่งฮวด อพาร์ทเมนท์ พัทยาใต้                               | lat=-84.9266 lon= 101.2443 | cat=landmark_and_historical_building
BTC construcciones                                           | lat=-84.8866 lon= 126.9432 | cat=
金門-小島良采（有機菇場）                                                | lat=-84.8666 lon=  14.7613 | cat=agricultural_cooperatives
Ser Esencia Restaurant                                       | lat=-84.8382 lon=-127.4854 | cat=restaurant
Amalia Medical Center Bima NTB                               | lat=-84.8184 lon=  36.8189 | cat=health_and_medical
Γυρο Γυρο                                                    | lat=-84.8157 lon= -17.1013 | cat=buffet_restaurant
Paitrika                                                     | lat=-84.8065 lon=  60.1172 | cat=fashion
Tahlia Marie                                                 | lat=-84.8039 lon= -77.3463 | cat=
Jay Shree Hadkai Maa Temple - Kodava                         | lat=-84.8027 lon=   0.6925 | cat=hindu_temple
Muhammad Hasri Videography                                   | lat=-84.8025 lon= -25.3125 | cat=professional_services
Iglesia Casa de Dios Restaurando Vidas para las Naciones .   | lat=-84.7969 lon=-112.6356 | cat=religious_organization
Countertops Gallery                                          | lat=-84.7690 lon= -37.6158 | cat=event_planning
Green Pc                                                     | lat=-84.7631 lon= 148.8537 | cat=computer_store
LPG Integral masažas Kaune                                   | lat=-84.7598 lon=  -6.0937 | cat=
bohemia_krsk                                                 | lat=-84.7454 lon=  15.4248 | cat=
Betomakgrup Beton Pompa                                      | lat=-84.7384 lon=  -4.2188 | cat=
A Tale of Four Mages                                         | lat=-84.7384 lon=   9.1406 | cat=print_media
Patuxent Range                                               | lat=-84.7167 lon= -64.5000 | cat=landmark_and_historical_building
Varenita of West Cobb                                        | lat=-84.7039 lon=  34.0019 | cat=retirement_home
South Beard Trading                                          | lat=-84.6842 lon=  39.6732 | cat=construction_services
McDonald's                                                   | lat=-84.6840 lon= -14.5949 | cat=fast_food_restaurant
Cidahu Camping Ground                                        | lat=-84.6735 lon=  -6.3291 | cat=sporting_goods
Villa's Caldos                                               | lat=-84.6735 lon= -36.5625 | cat=fast_food_restaurant
Vintage Cocktail Camper                                      | lat=-84.6735 lon=  18.9844 | cat=bar
Gabriela Solta - Photographer                                | lat=-84.6735 lon=  49.9219 | cat=professional_services
Cebu Capitol Central Hotel and Suites                        | lat=-84.6346 lon=  41.1447 | cat=hotel
King Ridge                                                   | lat=-84.6333 lon= -64.0833 | cat=landmark_and_historical_building
COFFE Station Old town                                       | lat=-84.6078 lon= 116.7188 | cat=cafe
SK1 Center                                                   | lat=-84.6068 lon=  28.6803 | cat=home_goods_store
Panaderia Doña Tere                                          | lat=-84.5583 lon=   6.6907 | cat=bakery
Injaz 20/20                                                  | lat=-84.5414 lon= -15.4688 | cat=school
Bárbara Ferreira Personal trainer                            | lat=-84.5414 lon=  -7.7344 | cat=gym
Yamu Store III                                               | lat=-84.5414 lon=   1.4062 | cat=music_venue
Make Noise Pro Audio LTD                                     | lat=-84.5414 lon=  23.2031 | cat=audio_visual_equipment_store
Dinas Kebudayaan dan Pariwisata Provinsi Jambi               | lat=-84.5413 lon=-137.1118 | cat=public_and_government_association
Woodstock Dentistry                                          | lat=-84.5392 lon=  34.1059 | cat=dentist
Tam-an Events Place and Hotel- Diffun, Quirino               | lat=-84.5215 lon=   8.7891 | cat=hotel
CALIRA & CHESKA  Villa & Resort                              | lat=-84.5161 lon= 164.7508 | cat=holiday_rental_home
โรงเรียนบ้านคดศอก                                            | lat=-84.5115 lon=  57.0688 | cat=school
Digital Computer                                             | lat=-84.4993 lon= 100.2607 | cat=computer_coaching
Abracadabra Technologie                                      | lat=-84.4741 lon=  46.4062 | cat=telecommunications_company
Restaurant Sawasdee                                          | lat=-84.4741 lon=  -0.7031 | cat=thai_restaurant
ณรงค์มอเตอร์แอร์                                             | lat=-84.4734 lon=  21.0856 | cat=automotive_repair
Quảng Cáo - In Nhanh SaPa                                    | lat=-84.4605 lon=-177.3281 | cat=
Oliveiras Trips                                              | lat=-84.4370 lon= -60.1491 | cat=tours
ไก่หมุนจิระภา สาขาสามแยกวัดป่าคา เฉลิมพระเกียรติ สระบุรี By ครกทอง | lat=-84.4367 lon= 178.4660 | cat=thai_restaurant
Japfa Experience                                             | lat=-84.4264 lon=  58.4581 | cat=restaurant
Tincorp Pa TI                                                | lat=-84.4059 lon=-178.5938 | cat=information_technology_company
絵画教室　みんなのアトリエ                                                | lat=-84.4000 lon= 142.3828 | cat=art_school
មានលក់សម្ភារៈផ្ទះបាយគ្រប់ប្រភេទ                              | lat=-84.3573 lon=  30.1849 | cat=installment_loans
โนนสำราญบ้านเฮา                                              | lat=-84.3433 lon= 140.4802 | cat=community_services_non_profits
Chennaicafelalajidilliwale                                   | lat=-84.3370 lon=   9.1406 | cat=theme_restaurant
Work N Wear International                                    | lat=-84.3112 lon=-171.6963 | cat=sports_wear
Alchemist Craftworks                                         | lat=-84.2951 lon=   6.3446 | cat=metal_fabricator
Gokul restaurant ode                                         | lat=-84.2673 lon= 128.1763 | cat=indian_restaurant
St. Isidore Catholic Learning Centre                         | lat=-84.2672 lon=  12.6562 | cat=
افكار فنية                                                   | lat=-84.2672 lon=  18.9844 | cat=arts_and_crafts
Yayasan Manbaul Hikmah Warrisalah                            | lat=-84.2231 lon= -67.2363 | cat=education
ร้านแว่นตาปราโมทย์ สาขาอารีย์                                | lat=-84.2107 lon=  42.0469 | cat=eyewear_and_optician
UAP Partners Sdn Bhd                                         | lat=-84.1965 lon= 117.7031 | cat=insurance_agency
La casa del deporte                                          | lat=-84.1545 lon=-135.4173 | cat=clothing_store
Cocomint  Mỹ Tho                                            | lat=-84.1438 lon=  90.7251 | cat=pizza_restaurant
In Aeternum Historia                                         | lat=-84.1257 lon= -94.9229 | cat=print_media
Angel Rising                                                 | lat=-84.1250 lon= 123.7500 | cat=
Alesha vandel                                                | lat=-84.0526 lon=  39.3750 | cat=professional_services
Fruit juice /smoothie/yoghurt                                | lat=-84.0389 lon=  28.4766 | cat=smoothie_juice_bar
Structura metalica Evobuild                                  | lat=-84.0280 lon=  11.1966 | cat=construction_services
Sallah Ganesh Enterprises                                    | lat=-84.0060 lon=  35.8886 | cat=
Elblesk e-mobility                                           | lat=-83.9793 lon=  -0.7031 | cat=automotive
Otago Potters Group                                          | lat=-83.9793 lon=   2.1094 | cat=
SMAN 1 Kedungadem                                            | lat=-83.9793 lon=  47.8125 | cat=school
Shelly Bhandari Makeup                                       | lat=-83.9788 lon= -56.9534 | cat=makeup_artist
Centro recreativo yuyako                                     | lat=-83.9638 lon= 116.5629 | cat=
Steve Kilbey                                                 | lat=-83.9508 lon= 166.1589 | cat=
Jones Valley                                                 | lat=-83.9167 lon= -56.8333 | cat=landmark_and_historical_building
Pololo GYM                                                   | lat=-83.9055 lon=-116.0148 | cat=gym
Mua Bàn Thờ                                                  | lat=-83.9051 lon= -10.5469 | cat=home_staging
Single Muslim Events                                         | lat=-83.9051 lon=  23.9062 | cat=social_service_organizations
Asha Physiotherapy Center                                    | lat=-83.8910 lon= 153.6438 | cat=physical_therapy
Matrioska Laços                                              | lat=-83.8696 lon= -62.5831 | cat=childrens_clothing_store
Ponpes al musthofawiyah                                      | lat=-83.8582 lon= -52.2949 | cat=school
Yoga Minh Thùy Quận 7  54/10 Lê Văn Lương                    | lat=-83.8299 lon=-116.0156 | cat=beauty_and_spa
Sumampong General Merchandise Inc.                           | lat=-83.8299 lon= 123.7500 | cat=hardware_store
Risen Savior Academy Davao City                              | lat=-83.8299 lon= 168.0469 | cat=school
Bamboo Espacio Cowork/Oficinas                               | lat=-83.8292 lon=  34.4533 | cat=real_estate
Doce Lembrança Decorações                                    | lat=-83.7814 lon= -46.6589 | cat=event_planning
Landi Servis Divjake                                         | lat=-83.7539 lon=  47.1095 | cat=
Experience Rarotonga                                         | lat=-83.7539 lon= -70.3124 | cat=tours
The Family Studio                                            | lat=-83.7539 lon= 125.1571 | cat=professional_services
리노성형외과                                                       | lat=-83.7350 lon= 101.7556 | cat=plastic_surgeon
Shree Aakash Electronics                                     | lat=-83.7255 lon= 154.6793 | cat=electronics
海港青年商會 JCI Harbour                                           | lat=-83.7114 lon= 153.2633 | cat=community_services_non_profits
Maquinarias HananCentenario                                  | lat=-83.7095 lon=  73.1250 | cat=machine_shop
Barbaro Negocios Inmobiliarios                               | lat=-83.6769 lon= -73.1250 | cat=real_estate
מכון טכנולוגי שמיר                                           | lat=-83.6769 lon= -11.7187 | cat=
Sund med Mia                                                 | lat=-83.6769 lon= -11.2500 | cat=professional_services
Buli Makhubo                                                 | lat=-83.6769 lon=  -3.5156 | cat=
Panti Sosial Tresna Werdha Budi Mulia 1 Ciracas              | lat=-83.6659 lon=  25.6858 | cat=public_service_and_government
IEQ Iguaçu I - Pr Ediberto Carvalho e Pra Consuela           | lat=-83.6381 lon= 105.4688 
[... truncated ...]estaurant
Trini Chatterbox for Movie & TV Show Reviews - PAGE          | lat=-61.9390 lon=  42.1875 | cat=professional_services
Live Life Loud Designs                                       | lat=-61.9390 lon= 153.4409 | cat=
Pudra Beauty Studio                                          | lat=-61.9264 lon=  73.3819 | cat=beauty_salon
Gam Point                                                    | lat=-61.9227 lon= -57.9546 | cat=landmark_and_historical_building
mocabonitaita                                                | lat=-61.9170 lon=  19.4306 | cat=
All Pro Plumbing                                             | lat=-61.9079 lon= 133.9453 | cat=plumbing
Tiệm Vải Huyền Võ                                            | lat=-61.9015 lon= -25.7008 | cat=
Oscar Junior Music                                           | lat=-61.8769 lon=  21.3838 | cat=
Sales Store                                                  | lat=-61.8769 lon= 169.8047 | cat=
Terapias Espirituales y Alternativas                         | lat=-61.8760 lon=  93.2562 | cat=naturopathic_holistic
Ténor store                                                  | lat=-61.8563 lon=  66.8828 | cat=mobile_phone_store
Saaol Heart Center Jalgaon                                   | lat=-61.8350 lon= 119.2607 | cat=cardiologist
Gahan Dental and health care                                 | lat=-61.8148 lon= -61.5973 | cat=
997ink Tattoo                                                | lat=-61.8118 lon=  53.3418 | cat=tattoo_and_piercing
Brits School of English                                      | lat=-61.7705 lon= 175.9131 | cat=language_school
Leomar Dancer                                                | lat=-61.7661 lon=  -7.8557 | cat=
VEROZ                                                        | lat=-61.7432 lon=  84.8837 | cat=fashion
Dừa Sáp Giá Sỉ                                               | lat=-61.7065 lon= -23.6250 | cat=home_service
Ace of fades barbershop                                      | lat=-61.6436 lon= 131.0593 | cat=barber
GNL Marketing                                                | lat=-61.6302 lon= 173.7872 | cat=marketing_agency
Mayra Barajas                                                | lat=-61.6145 lon= -39.3371 | cat=
Over The Top Yard Cards                                      | lat=-61.6106 lon=-122.0537 | cat=event_planning
Sri Ramanujar Engineering College - Vandalur, Chennai - 127. | lat=-61.6064 lon=-124.4531 | cat=college_university
Khanchana.jewellers                                          | lat=-61.6064 lon= -64.4531 | cat=fashion
Rebanho                                                      | lat=-61.6064 lon= -55.5469 | cat=food_beverage_service_distribution
Javastasia Distributors Halaal Meats                         | lat=-61.6064 lon= -30.9375 | cat=butcher_shop
Vision Global School                                         | lat=-61.6064 lon=   9.8438 | cat=school
Namestaj"PRIZENTA STIL"                                      | lat=-61.6064 lon=  23.2031 | cat=industrial_company
การจัดการ  มหาวิทยาลัยกรุงเทพสุวรรณภูมิ                      | lat=-61.6064 lon= 113.2031 | cat=college_university
GNZ7 Medical Corporation                                     | lat=-61.5650 lon= -34.7924 | cat=pharmacy
Latidos De Fe                                                | lat=-61.5308 lon= 179.6763 | cat=social_service_organizations
AKS Manufactory                                              | lat=-61.5202 lon= -97.7976 | cat=engineering_services
Escola De Condução Auto-Marmindo                             | lat=-61.5175 lon= -21.3354 | cat=school
البندقية سيتي                                                | lat=-61.5038 lon= -77.4202 | cat=mens_clothing_store
Alessandra Oliveira corretora de imóveis rj                  | lat=-61.4396 lon= -18.8793 | cat=real_estate
SD Luxury Salon By Sajid                                     | lat=-61.4051 lon=  79.8047 | cat=
Nông Sản Mai Cồ                                              | lat=-61.3827 lon= 152.8144 | cat=farm
Wing Tsun EWTO Algérie - Section Kouba                       | lat=-61.3547 lon= -83.9954 | cat=martial_arts_club
Teshal Gym Equipments Sri Lanka                              | lat=-61.3106 lon=  39.5918 | cat=gym
ဆုလဲ့ရည် အထည်ဆိုင် တောင်ငူ                                   | lat=-61.2832 lon=  43.5899 | cat=
Technology Store                                             | lat=-61.2725 lon= -11.2557 | cat=mobile_phone_store
美妍美容教育中心-Beauty in                                           | lat=-61.2702 lon= -28.5938 | cat=beauty_and_spa
HIDE                                                         | lat=-61.2702 lon=  -4.2188 | cat=event_planning
Federação Goiana de Breaking e Danças Desportivas            | lat=-61.2702 lon=  90.3516 | cat=community_services_non_profits
Creative Learners Art Studio                                 | lat=-61.2659 lon=  72.0418 | cat=tutoring_center
Shri Ram Marble Sirsa                                        | lat=-61.2657 lon=-127.5419 | cat=professional_services
AgriTracy Agricultural Fruit Seedlings                       | lat=-61.2280 lon=  56.1680 | cat=agricultural_service
İskele İşitme Merkezi                                        | lat=-61.2195 lon= -68.2702 | cat=health_and_medical
Ultrapremium Maroc                                           | lat=-61.1997 lon= 108.4570 | cat=pet_store
Vinh Food                                                    | lat=-61.1644 lon= -96.6797 | cat=fast_food_restaurant
Hair4all Indonesia                                           | lat=-61.1644 lon= -27.7734 | cat=shopping
Trung Tâm Kế Toán Bắc Ninh                                   | lat=-61.1602 lon=  41.8950 | cat=
លីដា លក់កណ្ដុរស្រែនិងត្រីងៀត                                 | lat=-61.1591 lon=  -2.1973 | cat=
Señor Waffle                                                 | lat=-61.1487 lon=-100.0827 | cat=breakfast_and_brunch_restaurant
Backwoods Bouquet                                            | lat=-61.1333 lon= -55.1167 | cat=
Healthdirect                                                 | lat=-61.1008 lon=-149.0910 | cat=school
Studio Nate Tattoo                                           | lat=-61.1008 lon= -36.5625 | cat=tattoo_and_piercing
Mandani Bay- by Leah Malobo                                  | lat=-61.0726 lon= -48.0997 | cat=real_estate_agent
Local Grill Abidjan                                          | lat=-61.0317 lon=  10.5469 | cat=restaurant
CA Dhiraj Ostwal                                             | lat=-61.0313 lon= -10.8994 | cat=fitness_trainer
Nejme al Maghrib sarl                                        | lat=-60.9751 lon=  87.7195 | cat=
RealEstate Patna                                             | lat=-60.9329 lon=  19.4610 | cat=real_estate
RJH property consultant                                      | lat=-60.9304 lon= -32.3328 | cat=real_estate
Renato Cake Designer                                         | lat=-60.9304 lon= -18.2812 | cat=candy_store
Captains Krill Oil                                           | lat=-60.9304 lon=  27.4219 | cat=health_food_store
Dope Footwear                                                | lat=-60.9304 lon=  93.8672 | cat=shoe_store
Global Parts Suppliers                                       | lat=-60.9279 lon= -31.2998 | cat=shopping
Paubril’s Beauty                                             | lat=-60.9196 lon=  33.7167 | cat=beauty_salon
Marcenaria Feijó                                             | lat=-60.9185 lon= -62.5906 | cat=
Sozoo Today                                                  | lat=-60.9156 lon= -24.9657 | cat=education
Kuya Insurance                                               | lat=-60.8996 lon=-142.5110 | cat=insurance_agency
Focus Photography                                            | lat=-60.8857 lon=  28.8298 | cat=professional_services
Valoriza Empreendimentos Imobiliários                        | lat=-60.8778 lon=  42.2363 | cat=real_estate
Laptop Galleria Kalibo                                       | lat=-60.8711 lon=  39.8665 | cat=computer_store
Blastic_007                                                  | lat=-60.8540 lon=-138.1619 | cat=
Buy Bak' Espacio Holístico                                   | lat=-60.8288 lon= 110.0391 | cat=naturopathic_holistic
Mughal Associates                                            | lat=-60.8280 lon=  -0.3517 | cat=real_estate_agent
corefitnesskonya                                             | lat=-60.8105 lon= -27.7062 | cat=gym
Amos Lake                                                    | lat=-60.7000 lon= -45.6500 | cat=landmark_and_historical_building
Foca Point                                                   | lat=-60.7000 lon= -45.6667 | cat=landmark_and_historical_building
Tattoos by Mauro Cornejo                                     | lat=-60.6733 lon=  25.5240 | cat=tattoo_and_piercing
Inner Sanctum                                                | lat=-60.6730 lon= -45.6260 | cat=landmark_and_historical_building
Plaerdemavida Cafetería                                      | lat=-60.6414 lon= -48.7538 | cat=coffee_shop
O Mundo da Ovelha BÉÉ                                        | lat=-60.6344 lon= -12.3492 | cat=ice_cream_shop
ជាងពិដាន-Ceilling                                            | lat=-60.5964 lon=  26.0596 | cat=
Transportes Fralse                                           | lat=-60.5901 lon=-106.5857 | cat=transportation
GTEC Computer Education Koppam                               | lat=-60.5872 lon= -14.0587 | cat=computer_coaching
Simersly Color                                               | lat=-60.5871 lon=   3.8672 | cat=
Grand Graha Selaras Bekasi                                   | lat=-60.5870 lon=-116.0156 | cat=real_estate
Rede Farmes Av.Minas Gerais                                  | lat=-60.5870 lon= -80.1562 | cat=pharmacy
AdminTax Co Ltd                                              | lat=-60.5870 lon=  -9.1406 | cat=professional_services
Flutura Beauty                                               | lat=-60.5870 lon= -18.2812 | cat=cosmetic_and_beauty_supplies
Rana Mohammadi Online Services خدمات آنلاین رنا محمدی        | lat=-60.5741 lon=  50.2378 | cat=tours
Lucky Homestay Nuwara Eliya                                  | lat=-60.5666 lon= 107.9712 | cat=hotel
Foul Point                                                   | lat=-60.5330 lon= -45.4830 | cat=landmark_and_historical_building
Taiyo-keito                                                  | lat=-60.5222 lon= 113.5657 | cat=shopping
Grupo Intertexto                                             | lat=-60.5194 lon= -65.5664 | cat=specialty_school
KYOSEI cooperativa sociale                                   | lat=-60.4980 lon=  17.5273 | cat=community_services_non_profits
Academic Assist Co.                                          | lat=-60.4927 lon= 148.1924 | cat=boutique
King World Product Store                                     | lat=-60.4919 lon=  29.7141 | cat=professional_services
Cương Audio                                                  | lat=-60.4872 lon= 170.8293 | cat=
Judith Borremans Natuurfotografie Workshops en Academie      | lat=-60.4706 lon= -45.3317 | cat=professional_services
Sashimi To-Go                                                | lat=-60.4328 lon= 107.7435 | cat=eat_and_drink
Secap spa                                                    | lat=-60.4297 lon=  75.0829 | cat=construction_services
Peluquería Gloria Frutillar                                  | lat=-60.4095 lon= 153.2806 | cat=beauty_and_spa
شماغ shemagh                                                 | lat=-60.4051 lon= -32.0482 | cat=fashion
Guru Nanak Bajaj-Delhi                                       | lat=-60.3734 lon=  89.9234 | cat=automotive_dealer
Aloj.arte.                                                   | lat=-60.3388 lon= -68.3362 | cat=event_planning
ลุงหมูขายคนกันเอง                                            | lat=-60.3106 lon=  41.8359 | cat=
Top Treinamentos                                             | lat=-60.2855 lon= -47.9609 | cat=education
Missis nail & Spa นิมมาน                                     | lat=-60.2516 lon= 107.1924 | cat=beauty_and_spa
Élida Santos Interiores                                      | lat=-60.2456 lon= 121.9084 | cat=
Splashback London                                            | lat=-60.2398 lon= -98.9824 | cat=interior_design
Babi fashons                                                 | lat=-60.2398 lon=-118.8773 | cat=clothing_store
Geraldine Seff                                               | lat=-60.2398 lon= -75.2344 | cat=tutoring_center
اقمشة طلال                                                   | lat=-60.2398 lon= -21.0938 | cat=fabric_store
Tramites al Volante                                          | lat=-60.2398 lon= 158.5547 | cat=driving_school
Rossi Denim Jeans                                            | lat=-60.2345 lon= -69.2528 | cat=womens_clothing_store
Seajet Paint USA                                             | lat=-60.2125 lon=  39.0234 | cat=boat_service_and_repair
Deplaymakeroficial                                           | lat=-60.2119 lon= -35.7377 | cat=
E17 Telecom                                                  | lat=-60.2071 lon= 150.4688 | cat=internet_service_provider
Pahuway Private Resort                                       | lat=-60.2002 lon=  78.0448 | cat=hotel
Thanh An Print - In Ấn Nhanh, Tiện, Chất Lượng               | lat=-60.1840 lon= -10.0128 | cat=
المحامي سرمد-  Law sarmad                                    | lat=-60.1751 lon= -32.1564 | cat=lawyer
Wimer-auto                                                   | lat=-60.1743 lon=-107.2266 | cat=
Ariel.União Veículos                                         | lat=-60.1705 lon=-107.5376 | cat=car_dealer
Lula Basics                                                  | lat=-60.1360 lon=  73.0399 | cat=
Al Herman Jewellers Bahawalpur                               | lat=-60.1218 lon= -70.0933 | cat=arts_and_entertainment
مركز د / كريم الجارحى للجلدية والتجميل والليزر               | lat=-60.0977 lon= -25.1087 | cat=hospital
Theint Brow and Beauty Academy                               | lat=-60.0819 lon=  -6.8290 | cat=beauty_and_spa
barberia_melli                                               | lat=-60.0651 lon=-134.1558 | cat=barber
Квіти з доставкою по Києву                                   | lat=-60.0629 lon= -59.3665 | cat=flowers_and_gifts_shop
Dani Motors                                                  | lat=-60.0484 lon=  14.4141 | cat=car_dealer
Easyworld                                                    | lat=-60.0389 lon= 132.9343 | cat=installment_loans
Quà tặng doanh nghiệp Trí Tuệ Việt                           | lat=-60.0298 lon=  25.4531 | cat=printing_services
Dra. Isadora Rosan                                           | lat=-60.0141 lon= -29.9775 | cat=
บัง BoB หญ้าด่วน                                             | lat=-60.0060 lon= -35.0804 | cat=soccer_field

6:21

OK, I see the issue. There seems to be a mismatch in how the bbox filtering is working. Many of the results seem to be in wrong locations. The geometry might have lat/lon swapped or something. Let me re-examine.

Looking at the original filter, I passed bbox=(-180, -90, 180, -60) which should get everything south of -60° latitude. But the results show places at lat=-89.9 which are clearly Antarctica coordinates... but lat=-61 with coordinates in North America, Europe, etc. That means the data has the lat/lon coordinates but Overture's bbox filtering might be working on their bbox column which could be different.

Wait, looking more carefully: some of the coordinate values look suspicious. For example:
- "BOSS Menswear Store" at lat=-86.8161, lon=36.1071 - that's in Antarctica if lat=-86 but in North Carolina/US if you swap.

Actually, I think the issue is that the geometry column has coordinates but some are coming through as (lon, lat) correctly. Let me re-check. Given bbox=(-180, -90, 180, -60)...

Actually looking at the real Antarctic stations:
- McMurdo at -77.85, 166.67 ✓
- South Pole area at -89.91
- Vostok at -78.46, 106.84 ✓
- Kunlun at -80.42, 77.12 ✓
- Concordia at -75.10, 123.33 ✓
- Mawson at -67.60, 62.87 ✓
- Dumont d'Urville at -66.66, 140.00 ✓
- Troll at -72.01, 2.54 ✓
- Neumayer at -70.65, -8.25 ✓
- Marambio at -64.24, -56.62 ✓
- Esperanza at -63.40, -57.00 ✓
- Carlini at -62.24, -58.67 ✓
- Bellingshausen at -62.20, -58.96 ✓
- Brown at -64.89, -62.87 ✓
- Palmer at -64.77, -64.05 ✓

So there ARE real stations. But we also see many false positives because Overture's place data includes the whole world's POIs and the bbox filter isn't working well (probably because the bbox column doesn't always match the geometry). Let me use a different approach.

Actually, looking at the data more carefully - many of these results have lat values near -60 but the lon corresponds to places in South America, Africa, etc. These might be places in southern Chile/Argentina or southern Africa that have lat slightly south of -60 but the place coordinates are actually wrong in Overture. OR - the bbox filter works on the `bbox` column which may have a different extent than the geometry point.

Given the messiness, let me take a different approach. Let me:
1. Query the data and filter by actual geometry coordinates (lat < -60)
2. Manually identify the known Antarctic research stations

Let me query more carefully.

6:21

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe
import pandas as pd

bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

# Filter by actual geometry y coordinate (latitude) < -60
mask = gdf.geometry.y < -60
gdf = gdf[mask].copy()

print(f'Features south of -60 deg: {len(gdf)}')

# Look at category distribution
cats = {}
for i, row in gdf.iterrows():
    c = row.get('categories', {})
    if isinstance(c, dict):
        p = c.get('primary', '')
        cats[p] = cats.get(p, 0) + 1

for c, n in sorted(cats.items(), key=lambda x: -x[1]):
    print(f'  {c}: {n}')
" 2>&1

Features south of -60 deg: 2348
  landmark_and_historical_building: 163
  fast_food_restaurant: 133
  mountain: 67
  professional_services: 58
  beauty_salon: 51
  school: 46
  real_estate: 41
  restaurant: 40
  shopping: 40
  hotel: 39
  beauty_and_spa: 30
  college_university: 29
  clothing_store: 27
  resort: 26
  education: 23
  hospital: 21
  womens_clothing_store: 20
  community_services_non_profits: 20
  automotive_repair: 19
  health_and_medical: 18
  shoe_store: 18
  car_dealer: 18
  event_planning: 18
  church_cathedral: 17
  furniture_store: 16
  cafe: 16
  religious_organization: 16
  public_and_government_association: 16
  arts_and_crafts: 14
  real_estate_agent: 14
  pharmacy: 13
  gym: 13
  mobile_phone_store: 13
  coffee_shop: 12
  arts_and_entertainment: 12
  tours: 11
  fashion: 10
  eat_and_drink: 10
  preschool: 10
  electronics: 10
  pet_store: 9
  motorcycle_dealer: 9
  public_service_and_government: 9
  dentist: 9
  structure_and_geography: 9
  retail: 8
  jewelry_store: 8
  automotive: 8
  bakery: 8
  supermarket: 8
  childrens_clothing_store: 8
  industrial_company: 8
  accommodation: 8
  fashion_accessories_store: 8
  construction_services: 8
  language_school: 7
  lawyer: 7
  lake: 7
  convenience_store: 7
  educational_services: 7
  travel_services: 7
  cosmetic_and_beauty_supplies: 6
  event_photography: 6
  motorcycle_repair: 6
  hardware_store: 6
  insurance_agency: 6
  home_cleaning: 6
  thai_restaurant: 6
  automotive_parts_and_accessories: 6
  real_estate_service: 6
  engineering_services: 6
  airport: 6
  food_delivery_service: 6
  japanese_restaurant: 6
  flowers_and_gifts_shop: 6
  desserts: 6
  spas: 6
  shopping_center: 6
  financial_service: 6
  tattoo_and_piercing: 6
  physical_therapy: 5
  art_school: 5
  beach: 5
  printing_services: 5
  mass_media: 5
  grocery_store: 5
  print_media: 5
  active_life: 5
  elementary_school: 5
  gas_station: 5
  doctor: 5
  computer_coaching: 5
  naturopathic_holistic: 5
  bar: 5
  boutique: 4
  pizza_restaurant: 4
  lounge: 4
  burger_restaurant: 4
  martial_arts_club: 4
  machine_shop: 4
  park: 4
  mens_clothing_store: 4
  caterer: 4
  agricultural_cooperatives: 4
  dance_school: 4
  interior_design: 4
  automotive_dealer: 4
  agriculture: 4
  private_school: 4
  holiday_rental_home: 4
  home_developer: 4
  catholic_church: 4
  fitness_trainer: 4
  bubble_tea: 4
  wholesale_store: 4
  non_governmental_association: 4
  sports_club_and_league: 4
  obstetrician_and_gynecologist: 4
  chicken_restaurant: 4
  installment_loans: 4
  social_service_organizations: 4
  middle_school: 4
  farm: 3
  italian_restaurant: 3
  day_care_preschool: 3
  tea_room: 3
  art_gallery: 3
  barber: 3
  home_goods_store: 3
  agricultural_service: 3
  campground: 3
  mexican_restaurant: 3
  auto_detailing: 3
  driving_school: 3
  computer_store: 3
  ice_cream_shop: 3
  public_utility_company: 3
  tutoring_center: 3
  lighting_store: 3
  music_school: 3
  beer_bar: 3
  makeup_artist: 3
  computer_hardware_company: 3
  public_school: 3
  b2b_textiles: 3
  home_service: 3
  eyewear_and_optician: 3
  sign_making: 3
  graphic_designer: 3
  library: 3
  laboratory_testing: 3
  commercial_industrial: 3
  industrial_equipment: 3
  media_news_company: 3
  painting: 3
  hvac_services: 3
  information_technology_company: 2
  sports_wear: 2
  high_school: 2
  psychotherapist: 2
  lingerie_store: 2
  town_hall: 2
  shaved_ice_shop: 2
  vitamins_and_supplements: 2
  building_supply_store: 2
  oral_surgeon: 2
  breakfast_and_brunch_restaurant: 2
  aquatic_pet_store: 2
  b2b_science_and_technology: 2
  transportation: 2
  topic_concert_venue: 2
  buddhist_temple: 2
  movers: 2
  software_development: 2
  yoga_studio: 2
  home_security: 2
  cultural_center: 2
  chinese_restaurant: 2
  home_improvement_store: 2
  pool_billiards: 2
  fruits_and_vegetables: 2
  plastic_manufacturer: 2
  family_practice: 2
  music_production: 2
  banks: 2
  freight_and_cargo_service: 2
  food_beverage_service_distribution: 2
  charity_organization: 2
  food: 2
  astrologer: 2
  psychologist: 2
  radio_station: 2
  thrift_store: 2
  internet_marketing_service: 2
  pentecostal_church: 2
  alcohol_and_drug_treatment_centers: 2
  plumbing: 2
  toy_store: 2
  bicycle_shop: 2
  butcher_shop: 2
  fabric_store: 2
  candy_store: 2
  chocolatier: 2
  wedding_planning: 2
  hindu_temple: 2
  computer_museum: 2
  sporting_goods: 2
  tire_dealer_and_repair: 2
  amateur_sports_team: 2
  campus_building: 2
  music_venue: 2
  lodge: 2
  health_food_store: 2
  employment_agencies: 2
  audio_visual_equipment_store: 2
  car_rental_agency: 2
  telecommunications_company: 2
  evangelical_church: 2
  self_storage_facility: 2
  indian_restaurant: 2
  sandwich_shop: 2
  topic_publisher: 2
  sports_and_recreation_venue: 2
  nutritionist: 2
  b2b_jewelers: 2
  river: 2
  legal_services: 2
  korean_restaurant: 2
  plastic_surgeon: 2
  museum: 2
  internet_service_provider: 2
  pet_groomer: 1
  antique_store: 1
  medical_service_organizations: 1
  brazilian_restaurant: 1
  optometrist: 1
  hair_salon: 1
  delicatessen: 1
  urban_farm: 1
  pet_breeder: 1
  cocktail_bar: 1
  indonesian_restaurant: 1
  stadium_arena: 1
  aircraft_repair: 1
  karaoke: 1
  trophy_shop: 1
  diagnostic_services: 1
  nursery_and_gardening: 1
  national_park: 1
  motel: 1
  panamanian_restaurant: 1
  auction_house: 1
  surgical_appliances_and_supplies: 1
  religious_school: 1
  office_equipment: 1
  party_supply: 1
  sewing_and_alterations: 1
  medical_center: 1
  landscaping: 1
  educational_camp: 1
  fountain: 1
  go_kart_club: 1
  sushi_restaurant: 1
  souvenir_shop: 1
  specialty_school: 1
  educational_research_institute: 1
  vietnamese_restaurant: 1
  powder_coating_service: 1
  shoe_repair: 1
  central_government_office: 1
  outlet_store: 1
  excavation_service: 1
  food_truck: 1
  boxing_class: 1
  cosmetology_school: 1
  race_track: 1
  bridal_shop: 1
  superstore: 1
  cabin: 1
  rental_service: 1
  shipping_center: 1
  bookstore: 1
  soccer_field: 1
  automation_services: 1
  scuba_diving_center: 1
  commercial_refrigeration: 1
  urologist: 1
  theatre: 1
  contractor: 1
  it_service_and_computer_repair: 1
  ambulance_and_ems_services: 1
  cupcake_shop: 1
  professional_sports_team: 1
  test_preparation: 1
  record_label: 1
  buffet_restaurant: 1
  home_staging: 1
  musical_instrument_store: 1
  image_consultant: 1
  weight_loss_center: 1
  horseback_riding_service: 1
  social_media_company: 1
  history_museum: 1
  winery: 1
  acupuncture: 1
  general_litigation: 1
  metal_fabricator: 1
  theme_restaurant: 1
  fish_farm: 1
  energy_company: 1
  hostel: 1
  rv_park: 1
  school_sports_team: 1
  investing: 1
  trailer_dealer: 1
  fertility: 1
  brokers: 1
  political_organization: 1
  dry_cleaning: 1
  smoothie_juice_bar: 1
  car_window_tinting: 1
  senior_citizen_services: 1
  pawn_shop: 1
  retirement_home: 1
  financial_advising: 1
  bank_credit_union: 1
  storage_facility: 1
  orthopedist: 1
  walk_in_clinic: 1
  venue_and_event_space: 1
  movie_television_studio: 1
  boat_service_and_repair: 1
  fishmonger: 1
  embassy: 1
  video_game_store: 1
  community_center: 1
  business_advertising: 1
  cosmetic_dentist: 1
  chiropractor: 1
  travel_company: 1
  laundromat: 1
  commercial_vehicle_dealer: 1
  water_treatment_equipment_and_services: 1
  archery_range: 1
  halal_restaurant: 1
  ear_nose_and_throat: 1
  psychic: 1
  e_cigarette_store: 1
  audiologist: 1
  mattress_store: 1
  food_stand: 1
  steakhouse: 1
  metal_plating_service: 1
  auto_company: 1
  energy_equipment_and_solution: 1
  wine_bar: 1
  veterinarian: 1
  architectural_designer: 1
  boat_rental_and_training: 1
  luggage_store: 1
  escape_rooms: 1
  mosque: 1
  cottage: 1
  train_station: 1
  pub: 1
  cardiologist: 1
  boat_parts_and_accessories: 1
  pilates_studio: 1
  diner: 1
  social_media_agency: 1
  beauty_product_supplier: 1
  emergency_room: 1
  monument: 1
  immigration_law: 1
  seafood_restaurant: 1
  marketing_agency: 1
  medical_research_and_development: 1

7:08

The problem is that the coordinates in Overture Places seem to have their lat/lon swapped in many cases (or there's a coordinate system mismatch). Places like "BOSS Menswear Store" at lat=-86.8161, lon=36.1071 are clearly in the northern hemisphere (Wisconsin area), but being shown with negative latitude. This suggests the coordinates might have lat/lon swapped somehow, or there's a WGS84 issue.

Let me look more carefully at the actual geometry and see what's happening. Maybe the issue is that some places are using (lon, lat) but others have them flipped. Let me look at the actual Antarctic stations that are correctly positioned.

7:08

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

# Check: These are known to be actual Antarctic stations
stations_to_check = [
    'Base McMurdo',
    'Bellingshausen Russian Antarctic Station', 
    'Carlini Base',
    'Esperanza Base, Antarctica',
    'Base Marambio, Antartida Argentina',
    'Neumayer-Station II',
    'Base antarctique Mawson',
    'Base antarctique Concordia',
    'Base antarctique Dumont-d\'Urville',
    'Brown Station',
    'Palmer Station, Antarctica',
    'Antarctic Kunlun Station',
    'Vostok İstasyonu',
    'Plateau Station',
    'Base antártica Concordia',
]

for s in stations_to_check:
    match = gdf[gdf['names'].apply(lambda x: isinstance(x, dict) and x.get('primary', '') == s)]
    if len(match) > 0:
        row = match.iloc[0]
        print(f'{s:55s} | geom: ({row.geometry.x:.4f}, {row.geometry.y:.4f})')
    else:
        print(f'{s:55s} | NOT FOUND')
" 2>&1

Base McMurdo                                            | geom: (166.6667, -77.8500)
Bellingshausen Russian Antarctic Station                | geom: (-58.9624, -62.1998)
Carlini Base                                            | geom: (-58.6668, -62.2379)
Esperanza Base, Antarctica                              | geom: (-56.9961, -63.3977)
Base Marambio, Antartida Argentina                      | geom: (-56.6198, -64.2413)
Neumayer-Station II                                     | geom: (-8.2500, -70.6500)
Base antarctique Mawson                                 | geom: (62.8736, -67.6026)
Base antarctique Concordia                              | geom: (123.5580, -75.1000)
Base antarctique Dumont-d'Urville                       | geom: (140.0013, -66.6628)
Brown Station                                           | geom: (-62.8700, -64.8950)
Palmer Station, Antarctica                              | geom: (-64.0531, -64.7743)
Antarctic Kunlun Station                                | geom: (77.1161, -80.4169)
Vostok İstasyonu                                        | geom: (106.8373, -78.4644)
Plateau Station                                         | geom: (40.5000, -79.2500)
Base antártica Concordia                                | geom: (123.3333, -75.1000)

7:33

Let me think about a better approach. The "places" theme doesn't have a specific "research station" category in Overture. Let me look at it differently - I should use the `infrastructure` type or even look at other themes for research stations. Actually, let me check what's available for Antarctic research stations.

Actually, let me think about what the Antarctic stations really are. In Overture, research stations might be captured differently. Let me look for infrastructure with relevant subtypes or use the division/land themes for getting the Antarctic land mass and water.

But actually, the key issue is: where do I get the Antarctic research station locations? Let me try a few approaches:

1. Look at Overture `infrastructure` for antarctic bases
2. Look for places with "station" or "base" in names that are actually in Antarctica
3. Use `division` data to get Antarctica boundaries

Let me also check for known Antarctic stations more carefully - looking at the actual coordinates.

7:33

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)

# Let's check infrastructure - look for subtypes
gdf = geodataframe('infrastructure', bbox=bbox, stac=True)
print('Subtypes:', gdf['subtype'].unique())
print()

# Check 'land' theme
gdf_land = geodataframe('land', bbox=bbox, stac=True)
print('Land columns:', list(gdf_land.columns))
print('Land shape:', gdf_land.shape)
if len(gdf_land) > 0:
    print('Land subtypes:', gdf_land['subtype'].unique() if 'subtype' in gdf_land.columns else 'no subtype')
print()

# Check 'water' theme
gdf_water = geodataframe('water', bbox=bbox, stac=True)
print('Water columns:', list(gdf_water.columns))
print('Water shape:', gdf_water.shape)
if len(gdf_water) > 0:
    print('Water subtypes:', gdf_water['subtype'].unique() if 'subtype' in gdf_water.columns else 'no subtype')
print()

# Check division_area
gdf_div = geodataframe('division_area', bbox=bbox, stac=True)
print('Division area columns:', list(gdf_div.columns))
print('Division area shape:', gdf_div.shape)
if len(gdf_div) > 0:
    print('Subtypes:', gdf_div['subtype'].unique() if 'subtype' in gdf_div.columns else 'no subtype')
" 2>&1

Subtypes: <ArrowStringArray>
[      'pedestrian',            'tower',          'airport',
   'transportation',       'recreation',             'quay',
             'pier',          'utility',    'communication',
          'barrier',            'power',            'water',
          'transit',           'bridge', 'waste_management',
        'aerialway']
Length: 16, dtype: str

Land columns: ['id', 'names', 'subtype', 'class', 'sources', 'source_tags', 'level', 'wikidata', 'surface', 'elevation', 'geometry', 'version', 'bbox']
Land shape: (93367, 13)
Land subtypes: <ArrowStringArray>
[    'rock',     'land', 'physical',  'glacier',   'crater',     'sand',
     'reef',  'wetland',    'shrub',    'grass']
Length: 10, dtype: str

Water columns: ['id', 'names', 'subtype', 'class', 'sources', 'source_tags', 'level', 'wikidata', 'is_intermittent', 'is_salt', 'geometry', 'version', 'bbox']
Water shape: (13501, 13)
Water subtypes: <ArrowStringArray>
[      'lake',   'physical',     'stream',      'ocean', 'human_made',
      'canal',       'pond',      'water',      'river',  'reservoir']
Length: 10, dtype: str

Division area columns: ['id', 'geometry', 'country', 'sources', 'subtype', 'admin_level', 'class', 'names', 'is_land', 'is_territorial', 'region', 'division_id', 'version', 'bbox']
Division area shape: (3, 14)
Subtypes: <ArrowStringArray>
['locality', 'country']
Length: 2, dtype: str

9:38

Good. Now I have an understanding of the data. Let me plan the approach:

1. **Get Antarctic research stations**: I need to use the `place` type (Overture `places` theme) to find Antarctic research stations. These are points with specific categories like `educational_research_institute`, or recognizable names. I'll filter for places south of -60 with station-related categories or names.

2. **Get Antarctic landmass**: Use `land` type (Overture `base` theme) - which has subtypes like `land`, `glacier`, `rock`, etc. I need the land polygons south of -60 to clip the station buffers.

3. **Get water features**: Use `water` type (Overture `base` theme) - and also `bathymetry` type (also from `base` theme) for ocean bathymetry.

4. **Projected coordinate system**: For Antarctica, the appropriate projection is EPSG:3031 (Antarctic Polar Stereographic) or EPSG:32761 (WGS 84 / Antarctic Polar Stereographic). Actually EPSG:3031 is the standard for Antarctica.

Let me think about the workflow:

**Step 1**: Download station locations (places south of -60 with appropriate categories)
**Step 2**: Download Antarctic land polygons
**Step 3**: Download water features
**Step 4**: Download bathymetry features
**Step 5**: For each station, create a 200km buffer in a suitable projected CRS
**Step 6**: Clip the buffers by land to get land-based zones
**Step 7**: Group overlapping land-clipped zones into coalitions
**Step 8**: For each station, intersect the buffer with water features to get over-water portions
**Step 9**: Write outputs as GeoParquet

Let me write a comprehensive script.

Actually, let me think about what categories constitute an Antarctic research station. The typical stations I see in the data:
- `Base McMurdo` (US) - landmark_and_historical_building
- `Bellingshausen Russian Antarctic Station` - educational_research_institute
- `Carlini Base` (Argentina) - arts_and_entertainment
- `Esperanza Base` (Argentina) - landmark_and_historical_building
- `Marambio Base` (Argentina) - airport / central_government_office
- `Neumayer-Station II` (Germany) - landmark_and_historical_building
- `Base antarctique Mawson` (Australia) - landmark_and_historical_building
- `Base antarctique Concordia` (France/Italy) - landmark_and_historical_building
- `Base antarctique Dumont-d'Urville` (France) - landmark_and_historical_building
- `Brown Station` (Argentina) - landmark_and_historical_building
- `Palmer Station` (US) - home_developer (misclassified)
- `Antarctic Kunlun Station` (China) - landmark_and_historical_building
- `Vostok Station` - landmark_and_historical_building
- `Plateau Station` (US) - landmark_and_historical_building
- `Troll Station` (Norway) - the 'Antarctica/Troll' entry
- `Syowa Station` (Japan) - not sure if present
- `Davis Station` (Australia)
- `Casey Station` (Australia)
- `Halley Station` (UK)
- `Rothera Station` (UK)
- `Mario Zucchelli Station` (Italy)
- `Jang Bogo Station` (South Korea)
- `King Sejong Station` (South Korea)
- `Great Wall Station` (China)
- `Zhongshan Station` (China)
- `Taishan Station` (China)
- `Mirny Station` (Russia)
- `Progress Station` (Russia)
- `Novolazarevskaya Station` (Russia)
- `Molodezhnaya Station` (Russia)
- `Vernadsky Station` (Ukraine)
- `San Martín Station` (Argentina)
- `Belgrano II Station` (Argentina)
- `Orcadas Station` (Argentina)
- `Comandante Ferraz Station` (Brazil)
- `Artigas Base` (Uruguay)
- `Escudero Base` (Chile)
- `Presidente Eduardo Frei Montalva Base` (Chile)
- `General Bernardo O'Higgins Base` (Chile)
- `Gabriel de Castilla Base` (Spain)
- `Juan Carlos I Base` (Spain)
- `Princess Elisabeth Base` (Belgium)
- `Dome Fuji Station` (Japan)
- `Mizuho Station` (Japan)
- `Aboa Station` (Finland)
- `Wasa Station` (Sweden)
- `Svea Station` (Sweden)
- `Tor Station` (Norway)
- `Troll Station` (Norway)
- `Mawson Station` (Australia)
- `Davis Station` (Australia)
- `Casey Station` (Australia)
- `Macquarie Island Station` (Australia)
- `Scott Base` (New Zealand)
- `Amundsen-Scott South Pole Station` (US)

I need to be more thorough about finding stations. Let me approach it differently - I'll search for place names containing station/base names and categorize them.

Actually, the better approach might be to search through all places that appear to be in Antarctica (south of -60), and look for those that are named as research stations/bases. Let me take a broader approach.

Let me write a comprehensive Python script that does this step by step.

9:38

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

# Filter south of -60
mask = gdf.geometry.y < -60
gdf = gdf[mask].copy()

# Let's look for station-like names more carefully
import re

# Known antarctic station keywords
station_kw = r'(station|base|research|camp|shelter|refuge|depot|observatory)'

hits = []
for i, row in gdf.iterrows():
    names = row.get('names', {})
    if isinstance(names, dict):
        name_val = names.get('primary', '')
        if name_val:
            name_lower = name_val.lower()
            if re.search(station_kw, name_lower) and ('antarct' in name_lower or row.geometry.y < -60):
                lat = row.geometry.y
                lon = row.geometry.x
                if lat < -60:
                    hits.append((name_val, lat, lon, row['id']))

for n, lat, lon, id_ in sorted(hits):
    print(f'{n:60s} | ({lat:8.4f}, {lon:9.4f})')
print(f'Total: {len(hits)}')
" 2>&1

Antarctic Kunlun Station                                     | (-80.4169,   77.1161)
Base Antártica Marambio                                      | (-64.2374,  -56.6235)
Base Esperanza, Antartida Argentina                          | (-63.3980,  -56.9974)
Base Marambio, Antartida Argentina                           | (-64.2413,  -56.6198)
Base McMurdo                                                 | (-77.8500,  166.6667)
Base antarctique Concordia                                   | (-75.1000,  123.5580)
Base antarctique Dumont-d'Urville                            | (-66.6628,  140.0013)
Base antarctique Mawson                                      | (-67.6026,   62.8736)
Base antarctique McMurdo                                     | (-77.8500,  166.6667)
Base antártica Concordia                                     | (-75.1000,  123.3333)
Bellingshausen Russian Antarctic Station                     | (-62.1998,  -58.9624)
Brown Station                                                | (-64.8950,  -62.8700)
Brown Station, Antarctic Peninsula                           | (-64.8921,  -62.8724)
COFFE Station Old town                                       | (-84.6078,  116.7188)
Camp Education Society's Dr. Arvind B. Telang IHM Chinchwad Pune - 19 | (-77.9157,  -12.6562)
Campo Creek Vineyards                                        | (-76.5168,    4.9219)
Carlini Base                                                 | (-62.2379,  -58.6668)
Cidahu Camping Ground                                        | (-84.6735,   -6.3291)
Dhacss Seaview Campus                                        | (-80.1786, -118.1243)
Escola Técnica de Teatro Professor José Gomes Campos         | (-77.0168,   52.1274)
Esperanza Base, Antarctica                                   | (-63.3977,  -56.9961)
Fuel Tech Micro Gas Station & Dispensers                     | (-65.3393,   15.4688)
González Videla Antarctic Base                               | (-64.8234,  -62.8575)
JRMSU Katipunan Campus CTEd-Laboratory High School           | (-69.1798,  123.5915)
Lakeview Clinical Research                                   | (-64.4135,  175.7812)
Marambio Base                                                | (-64.2410,  -56.6268)
Mario-Zucchelli-Station                                      | (-74.6943,  164.1125)
Master Bees Stationeries & Souvenirs                         | (-75.1408,  -66.7969)
Neumayer-Station II                                          | (-70.6500,   -8.2500)
New Lucena Municipal Police Station                          | (-80.8717,  -61.3477)
Noakhali Ideal Polytechnic Institute - Digital Campus        | (-73.4284,  -79.4531)
Palmer Station, Antarctica                                   | (-64.7743,  -64.0531)
Pioneer - Gas Station                                        | (-78.9098,   43.9264)
Plateau Station                                              | (-79.2500,   40.5000)
The Mahanaim - God's Camp                                    | (-72.4156,   11.3818)
VU GIFT Campus Jhang                                         | (-71.5249,   30.2344)
Vintage Cocktail Camper                                      | (-84.6735,   18.9844)
Total: 37

10:15

I need to find actual Antarctic research stations from Overture. The "place" dataset has them but they're categorized under various categories. Let me look at more systematic approaches - maybe "station" is a better keyword, and also look for stations from the "infrastructure" or "building" themes. Let me also look at all the place entries south of -60 more carefully.

Actually, the user said "research stations" - these are a specific category. In Overture, there's no specific "research_station" category. But I found several with names matching known stations. Let me use a comprehensive list of known Antarctic stations and search by name.

Let me also check the `building` theme and `division_area` for Antarctica.

Actually, I think the best approach is to:
1. Query Overture for `place` south of -60 latitude
2. Use a curated list of known Antarctic research station names to filter
3. Also possibly look at `building` features

Let me also check if there's data in the `division_area` for Antarctica boundaries.

10:15

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)

# Look at division_area for Antarctica
gdf = geodataframe('division_area', bbox=bbox, stac=True)
print('Shape:', gdf.shape)
if len(gdf) > 0:
    for i, row in gdf.iterrows():
        names = row.get('names', {})
        name_val = names.get('primary', '') if isinstance(names, dict) else ''
        print(f'  {name_val} | subtype={row.get(\"subtype\",\"\")} | region={row.get(\"region\",\"\")}')
" 2>&1

Shape: (3, 14)
  Rothera Research Station | subtype=locality | region=nan
  Antarctica | subtype=country | region=nan
  Antarctica | subtype=country | region=nan

10:49

OK, interesting - division_area has Antarctica as a country and Rothera Research Station as a locality. Let me now take a broader approach. Instead of trying to find all stations from 'place', let me look at all the data sources more carefully.

Actually, the best approach would be to use the overturemaps data more comprehensively. Let me:

1. Download `place` data south of -60 and filter for actual Antarctic research stations
2. Download `land` data for the Antarctic landmass
3. Download `water` data for water features
4. Download `bathymetry` data

Let me do this systematically. First, let me find all the actual Antarctic research stations from the 'place' dataset. I'll need to be more aggressive with filtering - looking for names that match known Antarctic stations.

Let me think about what Antarctic stations I know exist:

Major year-round stations:
- McMurdo (USA) - 77.85°S, 166.67°E
- Amundsen-Scott South Pole (USA) - 90°S
- Mawson (Australia) - 67.60°S, 62.87°E
- Davis (Australia) - 68.58°S, 77.97°E
- Casey (Australia) - 66.28°S, 110.53°E
- Halley (UK) - 75.58°S, 26.65°W
- Rothera (UK) - 67.57°S, 68.12°W
- SANAE IV (South Africa) - 71.67°S, 2.83°W
- Neumayer III (Germany) - 70.67°S, 8.27°W
- Syowa (Japan) - 69.00°S, 39.58°E
- Troll (Norway) - 72.01°S, 2.53°E
- Vostok (Russia) - 78.46°S, 106.84°E
- Mirny (Russia) - 66.55°S, 93.02°E
- Novolazarevskaya (Russia) - 70.77°S, 11.83°E
- Progress (Russia) - 69.37°S, 76.40°E
- Bellingshausen (Russia) - 62.20°S, 58.96°W
- Great Wall (China) - 62.22°S, 58.96°W
- Zhongshan (China) - 69.37°S, 76.37°E
- Kunlun (China) - 80.42°S, 77.12°E
- Taishan (China) - 73.86°S, 76.96°E
- Concordia (France/Italy) - 75.10°S, 123.35°E
- Dumont d'Urville (France) - 66.66°S, 140.00°E
- Mario Zucchelli (Italy) - 74.69°S, 164.12°E
- Jang Bogo (South Korea) - 74.62°S, 164.23°E
- King Sejong (South Korea) - 62.22°S, 58.79°W
- Carlini (Argentina) - 62.24°S, 58.64°W
- Esperanza (Argentina) - 63.40°S, 56.99°W
- Marambio (Argentina) - 64.24°S, 56.63°W
- San Martín (Argentina) - 68.13°S, 67.10°W
- Orcadas (Argentina) - 60.74°S, 44.75°W
- Brown (Argentina) - 64.90°S, 62.87°W
- Vernadsky (Ukraine) - 65.25°S, 64.25°W
- Palmer (USA) - 64.77°S, 64.05°W
- Arturo Prat (Chile) - 62.50°S, 59.68°W
- Bernardo O'Higgins (Chile) - 63.32°S, 57.90°W
- Presidente Eduardo Frei (Chile) - 62.20°S, 58.93°W
- Professor Escudero (Chile) - 62.21°S, 58.96°W
- Comandante Ferraz (Brazil) - 62.09°S, 58.40°W
- Henryk Arctowski (Poland) - 62.16°S, 58.47°W
- Gabriel de Castilla (Spain) - 62.98°S, 60.68°W
- Juan Carlos I (Spain) - 62.57°S, 60.39°W
- Scott Base (New Zealand) - 77.85°S, 166.76°E
- General Belgrano II (Argentina) - 77.87°S, 34.63°W
- Princess Elisabeth (Belgium) - 71.95°S, 23.35°E
- Machu Picchu (Peru) - 62.09°S, 58.47°W
- Law-Racoviță (Romania) - 69.39°S, 76.38°E
- Bharati (India) - 69.41°S, 76.19°E
- Maitri (India) - 70.77°S, 11.74°E
- Dakshin Gangotri (India) - 70.08°S, 12.00°E
- Aboa (Finland) - 73.04°S, 13.34°W
- Wasa (Sweden) - 73.05°S, 13.33°W
- Svea (Sweden) - 74.58°S, 11.22°W
- Tor (Norway) - 71.89°S, 5.19°E
- Jinnah (Pakistan) - 70.42°S, 25.72°E
- Showa... wait this is Syowa
- Molodyozhnaya (Russia) - 67.67°S, 45.85°E
- Leningradskaya (Russia) - 69.50°S, 159.38°E
- Russkaya (Russia) - 74.76°S, 136.85°W
- Vechernyaya (Belarus) - 67.73°S, 46.12°E

OK, I think the place dataset has limited coverage of research stations. Let me check if there's a better way. Let me look at the data more carefully and also check for areas (division_area) that might have Antarctic stations.

Actually, I found `Rothera Research Station` as a locality in division_area. Let me check if there are more.

10:49

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

# Let's check for Rothera in places
bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

# Search for rothera
mask = gdf['names'].apply(lambda x: isinstance(x, dict) and 'rothera' in x.get('primary', '').lower())
if mask.any():
    print('Rothera found in places')
    print(gdf[mask][['names', 'categories']].iloc[0])
else:
    print('Rothera not found in places')
    
# Search for other station names
station_names = ['Rothera', 'Halley', 'Syowa', 'Scott Base', 'Amundsen', 'South Pole',
                 'Maitri', 'Bharati', 'Princess Elisabeth', 'SANAE', 'Troll',
                 'Dumont', 'Concordia', 'Vostok', 'Mirny', 'Novolazarevskaya',
                 'Progress', 'Zhongshan', 'Great Wall', 'Jang Bogo', 'King Sejong',
                 'Mario Zucchelli', 'Vernadsky', 'Ferraz', 'Arctowski', 'McMurdo',
                 'Marambio', 'Esperanza', 'Bellingshausen', 'Carlini', 'O\'Higgins',
                 'Frei', 'Escudero', 'Arturo Prat', 'Gabriel de Castilla',
                 'Juan Carlos', 'Orcadas', 'San Martin', 'Brown', 'Palmer',
                 'Macquarie Island']

for s in station_names:
    mask = gdf['names'].apply(lambda x, s=s: isinstance(x, dict) and s.lower() in x.get('primary', '').lower())
    if mask.any():
        for _, row in gdf[mask].iterrows():
            names = row.get('names', {})
            name_val = names.get('primary', '') if isinstance(names, dict) else ''
            print(f'  Found: {name_val} at ({row.geometry.x:.4f}, {row.geometry.y:.4f})')
" 2>&1

Rothera not found in places
  Found: SANAE at (2.8500, -72.6667)
  Found: Troll Airfield at (2.4667, -71.9572)
  Found: Antarctica/Troll at (2.5350, -72.0114)
  Found: Base antarctique Dumont-d'Urville at (140.0013, -66.6628)
  Found: Base antártica Concordia at (123.3333, -75.1000)
  Found: Base antarctique Concordia at (123.5580, -75.1000)
  Found: Lake Vostok at (106.0000, -77.5000)
  Found: Vostok İstasyonu at (106.8373, -78.4644)
  Found: McMurdo General Hospital at (166.6693, -77.8468)
  Found: Base McMurdo at (166.6667, -77.8500)
  Found: Polární stanice McMurdo at (166.6667, -77.8500)
  Found: Base antarctique McMurdo at (166.6667, -77.8500)
  Found: Base Marambio, Antartida Argentina at (-56.6198, -64.2413)
  Found: Marambio Base at (-56.6268, -64.2410)
  Found: Base Antártica Marambio at (-56.6235, -64.2374)
  Found: Esperanza Base, Antarctica at (-56.9961, -63.3977)
  Found: Base Esperanza, Antartida Argentina at (-56.9974, -63.3980)
  Found: Bellingshausen Russian Antarctic Station at (-58.9624, -62.1998)
  Found: Carlini Base at (-58.6668, -62.2379)
  Found: Windfreight Express Total Logistics Inc.(WETLI) at (43.0664, -75.0730)
  Found: Brown Station, Antarctic Peninsula at (-62.8724, -64.8921)
  Found: Brown Station at (-62.8700, -64.8950)
  Found: Palmer Station, Antarctica at (-64.0531, -64.7743)

11:49

This is a complex task. Let me plan the approach:

1. **Get Antarctic research stations** - from Overture "place" type, south of -60° latitude. I need to identify actual Antarctic research stations. The Overture places data has names but not a specific "research station" category. I'll need to identify them by name patterns.

2. **Get Antarctic landmass** - from Overture "land" type (subtypes: rock, land, physical, glacier, crater, sand, reef, wetland, shrub, grass) - these represent land areas

3. **Get water features** - from Overture "water" type (subtypes: lake, physical, stream, ocean, human_made, canal, pond, water, river, reservoir) and "bathymetry" type

4. **Create 200 km buffers around stations** - in a suitable Antarctic projection

5. **Clip station spheres to land** - for the land-clipped coalition polygons

6. **Group into coalitions** where they overlap - union-then-dissolve approach

7. **Get over-water portion** - clip station spheres to water features

Let me think about the right Antarctica projection. The most common projected CRS for Antarctica is:
- EPSG:3031 (WGS 84 / Antarctic Polar Stereographic) - used for most of Antarctica
- EPSG:3976 (WGS 84 / Antarctic Polar Stereographic) - similar

EPSG:3031 is the standard choice for Antarctica.

For the station identification, I think I should take a more comprehensive approach. Let me look at common Antarctic stations and search more broadly.

Actually, let me take a practical approach:
1. Search Overture places for station-like names south of -60
2. Also check division_area for "Rothera Research Station" 
3. Get the geometry of stations

Let me also check what infrastructure gives us, maybe there are station-related features.

Actually, let me look at this more carefully. The Overture places data is crowdsourced/derived data and contains all sorts of POIs. For Antarctic stations, I should look at what's available more broadly.

Let me compile a definitive list of Antarctic research stations and see what's in Overture.

Known major Antarctic research stations:
- McMurdo Station (USA)
- Amundsen-Scott South Pole Station (USA)
- Palmer Station (USA)
- Halley Research Station (UK)
- Rothera Research Station (UK)
- Esperanza Base (Argentina)
- Marambio Base (Argentina)
- Carlini Base (Argentina)
- Bellingshausen Station (Russia)
- Vostok Station (Russia)
- Mirny Station (Russia)
- Novolazarevskaya Station (Russia)
- Progress Station (Russia)
- Mawson Station (Australia)
- Davis Station (Australia)
- Casey Station (Australia)
- Dumont d'Urville Station (France)
- Concordia Station (France/Italy)
- Mario Zucchelli Station (Italy)
- Syowa Station (Japan)
- Neumayer Station III (Germany)
- Troll Station (Norway)
- Tor Station (Norway)
- SANAE IV (South Africa)
- Bharati (India)
- Maitri (India)
- Great Wall Station (China)
- Zhongshan Station (China)
- Kunlun Station (China)
- Taishan Station (China)
- Jang Bogo Station (Korea)
- King Sejong Station (Korea)
- Vernadsky Research Base (Ukraine)
- Artigas Base (Uruguay)
- Comandante Ferraz (Brazil)
- Gabriel de Castilla (Spain)
- Juan Carlos I (Spain)
- Escudero Base (Chile)
- Presidente Eduardo Frei Montalva (Chile)
- Profesor Julio Escudero (Chile)
- General Bernardo O'Higgins (Chile)
- Teniente Rodolfo Marsh Martin (Chile)
- Princess Elisabeth Station (Belgium)
- Arctowski Station (Poland)
- Orcadas Base (Argentina)
- San Martín Base (Argentina)
- Belgrano II (Argentina)
- Petrel Base (Argentina)
- Deception Station (Argentina)
- Brown Station (Argentina)
- Macquarie Island Station (Australia)
- Molodezhnaya Station (Russia)
- Leningradskaya Station (Russia)

From the Overture data, I found quite a few. Let me now write a comprehensive script.

Actually, the Overpass API might be a better source for getting research stations, since Overture places may not have them all. But the user says "Pull stations from Overture." So let's work with what Overture gives us.

Let me look for stations more thoroughly.

11:49

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

mask = gdf.geometry.y < -60
gdf = gdf[mask].copy()

# Look for scientific/station related names more broadly
import re

# Comprehensive list of known Antarctic station name fragments
station_fragments = [
    'base', 'station', 'research', 'antarct', 'south pole', 'laboratory',
    'observatory', 'refuge', 'shelter', 'camp', 'depot'
]

hits = []
for i, row in gdf.iterrows():
    names = row.get('names', {})
    if isinstance(names, dict):
        name_val = names.get('primary', '')
        if name_val:
            name_lower = name_val.lower()
            # Must contain a station keyword AND be in Antarctica (lat < -60)
            if sum(1 for f in station_fragments if f in name_lower) >= 1:
                hits.append(name_val)

# Show unique hits sorted
for h in sorted(set(hits)):
    print(h)
" 2>&1

Antarctic Kunlun Station
Antarctic Peninsula
Antarctic Plateau
Antarctica, Antarctic Circle
Antarctica/Troll
Base Antártica Marambio
Base Esperanza, Antartida Argentina
Base Marambio, Antartida Argentina
Base McMurdo
Base antarctique Concordia
Base antarctique Dumont-d'Urville
Base antarctique Mawson
Base antarctique McMurdo
Base antártica Concordia
Beaver Lake (Antarctica)
Bellingshausen Russian Antarctic Station
British Antarctic Territory
Brown Station
Brown Station, Antarctic Peninsula
COFFE Station Old town
Camp Education Society's Dr. Arvind B. Telang IHM Chinchwad Pune - 19
Campo Creek Vineyards
Carlini Base
Chapel of the Blessed Virgin of Lujan, Antarctica
Cidahu Camping Ground
Cierva Cove, Antarctic Peninsula
Dhacss Seaview Campus
East Antarctica
Errera Channel, Antarctica
Escola Técnica de Teatro Professor José Gomes Campos
Esperanto Island, Antarctica
Esperanza Base, Antarctica
Fuel Tech Micro Gas Station & Dispensers
González Videla Antarctic Base
Grace-Care Laboratory Services and Supplies
JRMSU Katipunan Campus CTEd-Laboratory High School
King George Bay (Antarctica)
Lake Bonney (Antarctica)
Lake Glubokoye (Antarctica)
Lakeview Clinical Research
Lemaire Channel, Antarctic Peninsula
Marambio Base
Mario-Zucchelli-Station
Master Bees Stationeries & Souvenirs
Mikkelsen Harbour, Antarctic Peninsula
Mount Jackson (Antarctica)
Mount William (Antarctica)
Mount Wilson (Antarctica)
Neumayer-Station II
New Lucena Municipal Police Station
Nicholas Range (Antarctic)
Noakhali Ideal Polytechnic Institute - Digital Campus
Palmer Station, Antarctica
Paradise Bay, Antarctic Peninsula
Pioneer - Gas Station
Plateau Station
Port Lockroy, Antarctic Peninsula
Ross Ice Shelf, Antarctica
The Mahanaim - God's Camp
VU GIFT Campus Jhang
Vintage Cocktail Camper
West Antarctica

12:12

This is challenging because Overture's "place" data doesn't have a clean "research station" category for Antarctica. Let me take a different approach - I'll use OpenStreetMap data via Overture's infrastructure or building themes, or I'll use the division_area which already has "Rothera Research Station". Let me also check if there's a way to get Antarctic research stations more reliably.

Let me think about the approach differently. The task says "research stations" from Overture south of -60 latitude. Let me use the `place` data but be more targeted - looking for places that are actually research stations. The division_area has "Rothera Research Station" which is great.

Let me look at what's available in the OSM world through Overture, and also check what the `land` theme looks like for Antarctica (landmass).

Let me check building data too, or look at the actual structure better.

12:12

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

# Get buildings south of -60
bbox = (-180, -90, 180, -60)
bldg = geodataframe('building', bbox=bbox, stac=True)
print('Building shape:', bldg.shape)
print('Columns:', list(bldg.columns))
if len(bldg) > 0:
    print(bldg.head(3))
" 2>&1

Building shape: (1404, 24)
Columns: ['id', 'names', 'sources', 'level', 'height', 'min_height', 'is_underground', 'num_floors', 'num_floors_underground', 'min_floor', 'subtype', 'class', 'facade_color', 'facade_material', 'roof_material', 'roof_shape', 'roof_direction', 'roof_orientation', 'roof_color', 'roof_height', 'geometry', 'has_parts', 'version', 'bbox']
                                     id  ...                                               bbox
0  76c4a544-9ad3-4da0-be7d-22892dcbeb58  ...  {'xmin': -179.96853637695312, 'xmax': -179.963...
1  68c64c74-0704-4030-8d79-bb30b20fd032  ...  {'xmin': -167.40013122558594, 'xmax': -167.399...
2  34adb9da-7e11-4cc9-b5a6-53ae5518188b  ...  {'xmin': -138.461181640625, 'xmax': -138.45275...

[3 rows x 24 columns]

13:05

Let me think about this problem more carefully.

The user wants:
1. Antarctic research stations south of -60 latitude
2. Each station gets a notional 200 km operational radius
3. Two output files:
a. `station_spheres.geoparquet` - land-clipped station zones grouped into coalitions wherever they overlap. Each coalition gets an integer `coalition` id. Every feature needs `station_id` and `station_name`.
b. `station_water_overlap.geoparquet` - over-water portion of each station's zone, attributed with the water features it intersects. Each row has `station_id`, `station_name`, `water_id`, `water_name`, `water_subtype`, and `water_source` (literal values `base.water` or `base.bathymetry`).

Let me plan:

1. Get Antarctic research stations from Overture. The best sources:
- Places (place theme) with station-related names
- Infrastructure might also have station info
- Division_area has "Rothera Research Station"

2. For the Antarctic landmass, I can use the `land` theme from Overture (subtype='land' gives Antarctic continent)
3. For water, use `water` theme and `bathymetry` theme from Overture

4. Project to an appropriate projected CRS for Antarctica - likely EPSG:3031 (Antarctic Polar Stereographic) which is the standard for Antarctica.

Let me think about which places are actual research stations. The Overture Places data has some entries with names like "Bellingshausen Russian Antarctic Station" categorized as 'educational_research_institute', and several entries labeled as 'landmark_and_historical_building' that are clearly research stations.

Let me build a comprehensive approach: I'll get all Overture place data south of -60, identify research stations by name patterns and known station lists, then create the 200km buffers.

Actually, let me also check the `division_area` for Rothera - it was found there. And let me check `infrastructure` more carefully.

Let me take a step back and write a comprehensive script.

Key data sources from Overture:
- Places: For research stations
- Land: For Antarctic landmass (subtype='land', 'glacier', 'rock', etc.)
- Water: For water features (subtype='lake', 'ocean', 'river', etc.)
- Bathymetry: For bathymetric features (ocean floor)

The approach:

1. Download research stations from Overture places, identified by name/keywords
2. Download Antarctic land from Overture land theme
3. Download water features from Overture water theme
4. Download bathymetry from Overture bathymetry theme
5. Project to EPSG:3031 (Antarctic Polar Stereographic)
6. Create 200km buffers around each station
7. Clip buffers to land to get land-clipped zones
8. Union overlapping land-clipped zones into coalitions
9. For water part, clip buffers to water, intersect with water features

Let me code this up. But first, I need to figure out the research stations more carefully.

Actually, the user said "Antarctic research stations" - so I need to find actual research stations. The Overture Places data south of -60 has many entries, many of which are clearly not research stations (like pizza places, beauty salons, etc. - these appear to be data quality issues where coordinates might be wrong).

A more reliable approach would be to use a known list of Antarctic research stations, or filter by relevant categories. Let me check what's available.

From the division_area, I found "Rothera Research Station" and from places we have actual stations. Let me collect all stations systematically.

I think the best approach is to use multiple Overture themes to identify stations:
1. Places with relevant categories (educational_research_institute, landmark_and_historical_building in Antarctica)
2. Division_area (has Rothera)
3. Infrastructure (may have station-related entries)

But the places data has lots of noise. Let me use a curated approach with known station names plus Overture data.

Let me write the full script now.

13:05

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

# Check bathymetry
bbox = (-180, -90, 180, -60)
bathy = geodataframe('bathymetry', bbox=bbox, stac=True)
print('Bathymetry shape:', bathy.shape)
print('Columns:', list(bathy.columns))
if len(bathy) > 0:
    print('Subtypes:', bathy['subtype'].unique() if 'subtype' in bathy.columns else 'N/A')
    print(bathy.head(2))
" 2>&1

Bathymetry shape: (3096, 7)
Columns: ['id', 'depth', 'cartography', 'geometry', 'sources', 'version', 'bbox']
Subtypes: N/A
                                     id  ...                                               bbox
0  1a3d446c-2614-5ef5-bfa7-6508b75d1504  ...  {'xmin': -179.2291717529297, 'xmax': -179.0039...
1  60b4800b-67ca-54fe-a99f-7818470c5bf3  ...  {'xmin': -179.19097900390625, 'xmax': -179.042...

[2 rows x 7 columns]

13:24

Alright, let me design the full solution. Here's my approach:

1. **Pull stations**: I'll use `place` data from Overture, identify stations by filtering for names that contain station/base keywords and locations south of -60° latitude.

2. **Pull landmass**: Use `land` theme to get Antarctic land features.

3. **Pull water features**: Use `water` and `bathymetry` themes.

4. **Create 200 km buffer zones** around each station, clip to land, union overlapping ones into coalitions.

5. **For water overlaps**: Take station zones, subtract land, intersect with water features, attribute with water info.

Let me write this as a proper Python script. I need an appropriate projected CRS for Antarctica. The EPSG:3031 (Antarctic Polar Stereographic) is the standard choice for Antarctica.

Let me also think about what constitutes a "research station" vs just a place with "station" in the name. I'll use a targeted approach - either filter by name keywords or use the known station identifiers from the specific list.

Actually, let me think about this more carefully. The user said "research stations" - in Overture's places data, the actual research stations appear with various categories. Let me use a broader heuristic: any place south of -60° whose primary name contains station/base/refuge/camp/research AND is clearly in Antarctica (not coincidental name matches from other continents with wrong geometry).

Actually, looking at the data more carefully, there are clearly many misattributed coordinates. The Overture places south of -60 all seem to have the correct geometry (negative y = south), but many coordinates are clearly wrong (like items at lat=-84 but named after places in India). This is likely because the place data has some coordinate artifacts.

Let me take a different approach - I'll look for actual Antarctic research stations. Let me compile a proper list based on known Antarctic stations rather than trying to guess from keywords.

Actually, let me look at the actual places in Overture that are genuine Antarctic research stations. Looking at the results:

- Base McMurdo (-77.85, 166.67) ✓
- Bellingshausen Russian Antarctic Station (-62.20, -58.96) 
- Carlini Base (-62.24, -58.67)
- Esperanza Base (-63.40, -56.99)
- Base Marambio (-64.24, -56.62)
- Neumayer-Station II (-70.65, -8.25)
- Base antarctique Mawson (-67.60, 62.87)
- Base antarctique Concordia (-75.10, 123.56)
- Base antarctique Dumont-d'Urville (-66.66, 140.00)
- Brown Station (-64.90, -62.87)
- Palmer Station (-64.77, -64.05)
- Antarctic Kunlun Station (-80.42, 77.12)
- Vostok İstasyonu (-78.46, 106.84)
- Plateau Station (-79.25, 40.50)
- Antarctica/Troll (-72.01, 2.54)
- Mario-Zucchelli-Station (-74.69, 164.11)
- González Videla Antarctic Base (-64.82, -62.86)

These are all valid Antarctic stations. But I'm missing many like Halley, Scott Base, Amundsen-Scott South Pole, SANAE, Syowa, etc.

Let me take a more systematic approach. I'll get ALL places south of -60, filter them manually by looking at their categories and names to identify genuine research stations. But first let me check if there are other themes that might have station data.

Actually, looking at the `division_area` data, it found "Rothera Research Station" as a locality! Let me try to get all division_areas.

Let me check if the division_area has more entries.

13:24

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

# Check division_area for more detail
bbox = (-180, -90, 180, -60)
gdf = geodataframe('division_area', bbox=bbox, stac=True)
print('Shape:', gdf.shape)
print()
for i, row in gdf.iterrows():
    names = row.get('names', {})
    name_val = names.get('primary', '') if isinstance(names, dict) else ''
    sub = row.get('subtype', '')
    adm = row.get('admin_level', '')
    print(f'{name_val} | subtype={sub} | admin_level={adm}')
" 2>&1

Shape: (3, 14)

Rothera Research Station | subtype=locality | admin_level=nan
Antarctica | subtype=country | admin_level=0.0
Antarctica | subtype=country | admin_level=0.0

13:56

Let me now think about the approach comprehensively.

The task is to:
1. Get Antarctic research stations from Overture data (south of -60°)
2. Get Antarctic landmass and water features
3. Create 200 km buffers around each station
4. Clip station buffers to land → group overlapping land-clipped buffers into coalitions → output `station_spheres.geoparquet`
5. For each station, find the over-water portion of its 200 km buffer, intersect with water features, attribute with water info → output `station_water_overlap.geoparquet`

Key considerations:
- Antarctic stations from Overture places - I need to identify which places are actual Antarctic research stations. The "place" theme has categories but many Antarctic stations are oddly categorized. Let me use a combination of known names, the "place" data, and also division_area (which has "Rothera Research Station" as a locality).
- Some stations like Mario Zucchelli, SANAE, Troll, Neumayer etc are represented as landmarks or have no dedicated entry. I should build a comprehensive list.

Let me be more thorough. I'll look at the known Antarctic research stations from COMNAP (Council of Managers of National Antarctic Programs) and find them in the Overture data. But actually, I think a cleaner approach is to pull ALL place entries south of -60, filter for station/Base/research ones, AND also look at division_area (which has Rothera), and infrastructure (which might have more).

Actually, let me think about this more carefully. The user said "pull stations from Overture south of -60 latitude." So I should identify actual research stations from the Overture data. Let me look at what's available more systematically.

Let me check for some more known station names.

13:56

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe

bbox = (-180, -90, 180, -60)
gdf = geodataframe('place', bbox=bbox, stac=True)

mask = gdf.geometry.y < -60
gdf = gdf[mask].copy()

# Let's look at the 'names' field more carefully for all entries
# and also check the 'sources' and 'addresses' fields
print('sources sample:', gdf['sources'].iloc[0] if len(gdf) > 0 else 'N/A')
print()

# Let's look at the full names dict for a few station-like entries
station_entries = gdf[gdf['names'].apply(lambda x: isinstance(x, dict) and any(kw in x.get('primary', '').lower() for kw in ['station', 'base', 'research', 'antarct', 'laboratory']))]
for i, row in station_entries.head(20).iterrows():
    names = row.get('names', {})
    src = row.get('sources', {})
    print(f'{names.get(\"primary\",\"\"):60s} | sources={src}')
" 2>&1

sources sample: [{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '489227217768882', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.2135734260082245, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]

Ross Ice Shelf, Antarctica                                   | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '103588944794009', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.7038461565971375, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
West Antarctica                                              | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '140743262617813', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.7446495294570923, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Antarctic Peninsula                                          | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '102889719752817', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.8250200152397156, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}
 {'property': '/properties/operating_status', 'dataset': 'Overture-signals', 'license': 'CDLA-Permissive-2.0', 'record_id': '691b52d4-63bc-4a88-b835-a53935c95c54', 'update_time': '2026-05-12T00:29:08Z', 'confidence': 1.0, 'between': array([nan, nan])}]
Mount Wilson (Antarctica)                                    | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '118974618192205', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.792350709438324, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Master Bees Stationeries & Souvenirs                         | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '615877639066675', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.9114405512809753, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Errera Channel, Antarctica                                   | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '101551795379918', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.7289046049118042, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Paradise Bay, Antarctic Peninsula                            | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '100738911717025', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.752984881401062, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Brown Station, Antarctic Peninsula                           | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '100443595880780', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.7136415839195251, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Brown Station                                                | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '140321252661179', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.8140439987182617, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
González Videla Antarctic Base                               | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '1416601808651739', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.6997304558753967, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Port Lockroy, Antarctic Peninsula                            | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '107012604461029', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.703095018863678, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Mount William (Antarctica)                                   | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '178307318874880', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.7833900451660156, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Lemaire Channel, Antarctic Peninsula                         | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '113698713790449', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.7433815002441406, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Palmer Station, Antarctica                                   | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '396891237758501', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.6004300117492676, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Mount Jackson (Antarctica)                                   | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '133064190063333', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.7991982102394104, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
New Lucena Municipal Police Station                          | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '358397245094626', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.8010992407798767, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Esperanto Island, Antarctica                                 | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '107960474150152', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.7901460528373718, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Cierva Cove, Antarctic Peninsula                             | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '106764964474076', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.703281044960022, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Antarctica, Antarctic Circle                                 | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '111892170612665', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.7410102486610413, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]
Mikkelsen Harbour, Antarctic Peninsula                       | sources=[{'property': '', 'dataset': 'meta', 'license': 'CDLA-Permissive-2.0', 'record_id': '116375750193765', 'update_time': '2026-04-27T00:00:00.000Z', 'confidence': 0.721491813659668, 'between': None}
 {'property': '/properties/confidence', 'dataset': 'Overture', 'license': 'CDLA-Permissive-2.0', 'record_id': None, 'update_time': '2026-05-13T20:48:36Z', 'confidence': None, 'between': None}]

15:32

Now I have a good understanding. Let me check what water data is available for water theme and bathymetry theme, and best project CRS for Antarctica.

For Antarctica, the standard projected coordinate systems are:
- Antarctic Polar Stereographic (EPSG:3031) - used for the whole continent
- The bounding box approach

Let me now design the comprehensive pipeline:

1. Download stations - I need to find research stations. Looking at the places data, Overture has place features for many stations. Let me use a combination approach - look at places with relevant names AND at division_area.

Actually, thinking about this more carefully, I should use the 'place' theme to find research stations. The key known stations I identified include:
- McMurdo (US)
- Amundsen-Scott (US) - South Pole
- Bellingshausen (Russia)
- Carlini (Argentina)
- Esperanza (Argentina)
- Marambio (Argentina)
- Neumayer (Germany)
- Mawson (Australia)
- Concordia (France/Italy)
- Dumont d'Urville (France)
- Brown (Argentina)
- Palmer (US)
- Kunlun (China)
- Vostok (Russia)
- Plateau (US)
- Mario Zucchelli (Italy)
- SANAE (South Africa)
- Troll (Norway)
- Halley (UK) - NOT found in Overture
- Rothera (UK) - only in division_area

Let me think about a more robust approach. I'll search for any place with geometry south of -60 that has a name matching known station patterns. But I also need to ensure I'm not picking up false positives like "COFFE Station Old town" etc.

Actually, let me reconsider. The user said "Antarctic research stations". Let me pull them from Overture. Overture has a place theme that includes research stations. Let me also check if there's a better way - maybe look for categories that include 'research' or look at the Wikidata entries.

Actually, a simpler and more comprehensive approach: I'll look for all 'place' entries south of -60 with any of the known station names in their name, OR entries that are tagged with research/institutional categories. But actually the cleanest approach might be to just identify all known Antarctic research stations and get their coordinates.

Let me think about this more practically. The user wants "stations from Overture south of -60 latitude". So I just need to query the Overture places dataset for anything south of -60 that is a research station or base. The issue is that the Overture places categories are not standardized well for Antarctic stations.

Let me take a pragmatic approach: I'll query all places south of -60, and filter for those whose names match known Antarctic station patterns. I'll also look at the division_area for Rothera which was tagged as a locality.

Actually, let me reconsider the whole approach. Instead of trying to identify stations by name matching, which is fragile and will miss many, let me look for what Overture has. Let me check what categories/structure exists for the "place" entries that are actually stations.

Looking at it more carefully, several stations are tagged as "landmark_and_historical_building" which is not helpful. Others are tagged as "educational_research_institute" (Bellingshausen), "airport" (Marambio, Esperanza), "arts_and_entertainment" (Carlini), "home_developer" (Palmer), "central_government_office" (Marambio), etc. So there's no single category to filter on.

The practical approach is to compile a list of known Antarctic research stations and find their locations via Overture's place data. Let me compile a comprehensive list.

Actually, I think the best approach is:
1. Query all places from Overture south of -60
2. Use a comprehensive list of known station names/keywords to filter
3. For stations not found in places, check division_area

Let me write the full script now.

15:32

Write /work/solve.py

content · 595 lines · py

#!/usr/bin/env python3
"""
Cross-station logistics-overlap maps for Antarctic Treaty consultative meeting.

Produces two GeoParquet outputs in EPSG:3031 (Antarctic Polar Stereographic):
  1. station_spheres.geoparquet   – land-clipped station zones, grouped into
     coalitions wherever they overlap.  Columns: coalition (int), station_id,
     station_name, geometry (MultiPolygon).
  2. station_water_overlap.geoparquet – over-water portions of each station's
     200 km buffer, attributed with water features intersected.  Columns:
     station_id, station_name, water_id, water_name, water_subtype,
     water_source, geometry (MultiPolygon).
"""

import itertools
import logging
import sys
import time

import geopandas as gpd
import numpy as np
import pandas as pd
import pyarrow.compute as pc
import shapely
from overturemaps.core import geodataframe, record_batch_reader
from shapely.geometry import MultiPolygon, Point, Polygon, mapping, shape
from shapely.ops import unary_union

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
log = logging.getLogger(__name__)

# ── Antarctic bounding box (lon_min, lat_min, lon_max, lat_max) ──────────────
ANT_BBOX = (-180.0, -90.0, 180.0, -60.0)

# ── Output CRS (Antarctic Polar Stereographic) ───────────────────────────────
OUT_CRS = "EPSG:3031"

# ── Buffer radius in metres ──────────────────────────────────────────────────
BUFFER_M = 200_000  # 200 km

# =============================================================================
# 1.  Gather Antarctic research stations from Overture
# =============================================================================

# A comprehensive set of known Antarctic research station name fragments.
# We match against the lower-cased primary name of each place.
STATION_KEYWORDS = frozenset(
    kw.lower()
    for kw in [
        # Major year-round stations
        "mcmurdo",
        "amundsen-scott",
        "amundsen scott",
        "south pole station",
        "south pole",
        "scott base",
        "mawson",
        "davis",
        "casey",
        "halley",
        "rothera",
        "san martin",
        "progress",
        "novolazarevskaya",
        "vostok",
        "concordia",
        "kunlun",
        "troll",
        "tor",
        "neumayer",
        "kohnen",
        "princess elisabeth",
        "syowa",
        "mizuho",
        "asuka",
        "dumont d'urville",
        "dumont-d'urville",
        "mirny",
        "bellingshausen",
        "great wall",
        "zhongshan",
        "taishan",
        "jang bogo",
        "king sejong",
        "sejong",
        "mario zucchelli",
        "zucchelli",
        "vernadsky",
        "carlini",
        "esperanza",
        "marambio",
        "orcad",
        "orcadas",
        "brown",
        "palmer",
        "molodezhnaya",
        "leningradskaya",
        "byrd station",
        "byrd",
        "plateau station",
        "plateau",
        "siple",
        "sanae",
        "maitri",
        "bharati",
        "ferraz",
        "ohiggins",
        "frei",
        "escudero",
        "artigas",
        "prat",
        "gabriel de castilla",
        "juan carlos i",
        "juan carlos",
        "melchior",
        "petrel",
        "decepcion",
        "matienzo",
        "primavera",
        "camara",
        "belgrano",
        "teniente",
        "comandante ferraz",
        "gonzalez videla",
        "gonzález videla",
        "general belgrano",
    ]
)


def fetch_stations():
    """Fetch Antarctic research station locations from Overture 'place' data."""
    log.info("Fetching Overture places south of -60° …")
    places = geodataframe("place", bbox=ANT_BBOX, stac=True)

    # Keep only features south of -60°
    mask = places.geometry.y < -60.0
    places = places[mask].copy()
    log.info("  %d place features south of -60°", len(places))

    stations = []
    matched_ids = set()

    for _, row in places.iterrows():
        names = row.get("names")
        if not isinstance(names, dict):
            continue
        name = names.get("primary", "")
        if not name:
            continue
        name_lower = name.lower()

        # Check if this name contains any station keyword
        # Use a more careful check to avoid false positives like "COFFE Station Old town"
        # We require either that the full name matches a station keyword,
        # OR the name ends with "station" or "base" and is in Antarctica
        is_match = False
        for kw in STATION_KEYWORDS:
            if kw in name_lower:
                is_match = True
                break

        # Additional heuristic: if name contains "base" or "station" or "research"
        # and is in the Antarctic region, include it
        if not is_match and any(
            word in name_lower for word in ["base ", " station", "research station"]
        ):
            if row.geometry.y < -60:
                is_match = True

        if is_match:
            stations.append(
                {
                    "station_id": row["id"],
                    "station_name": name,
                    "geometry": row.geometry,
                    "lat": row.geometry.y,
                    "lon": row.geometry.x,
                }
            )
            matched_ids.add(row["id"])

    # Also check the division_area table for Rothera etc.
    log.info("Fetching Overture division_area …")
    div = geodataframe("division_area", bbox=ANT_BBOX, stac=True)
    for _, row in div.iterrows():
        names = row.get("names")
        if not isinstance(names, dict):
            continue
        name = names.get("primary", "")
        if not name:
            continue
        if row["id"] in matched_ids:
            continue
        name_lower = name.lower()
        if any(
            kw in name_lower
            for kw in ["rothera", "station", "base", "research"]
        ):
            if row.geometry.centroid.y < -60:
                stations.append(
                    {
                        "station_id": row["id"],
                        "station_name": name,
                        "geometry": row.geometry.centroid,
                        "lat": row.geometry.centroid.y,
                        "lon": row.geometry.centroid.x,
                    }
                )
                matched_ids.add(row["id"])

    # Deduplicate by station_name
    seen = set()
    unique = []
    for s in stations:
        key = s["station_name"].lower().strip()
        if key not in seen:
            seen.add(key)
            unique.append(s)

    log.info("  Found %d unique stations", len(unique))
    for s in sorted(unique, key=lambda x: x["station_name"]):
        log.info("    %s  (%.4f, %.4f)", s["station_name"], s["lon"], s["lat"])

    return unique


# =============================================================================
# 2.  Fetch Antarctic landmass and water features
# =============================================================================


def fetch_land():
    """Fetch Antarctic land polygons from Overture 'land' theme."""
    log.info("Fetching Overture land data …")
    land = geodataframe("land", bbox=ANT_BBOX, stac=True)

    # Filter to keep only south of -60
    mask = land.geometry.y < -60.0
    land = land[mask].copy()
    log.info("  %d land features south of -60°", len(land))

    # Reproject to OUT_CRS
    land = land.to_crs(OUT_CRS)

    # Dissolve into a single land polygon (or multi-polygon)
    land_union = unary_union(land.geometry.values)
    log.info("  Land union done")

    return land_union


def fetch_water():
    """Fetch Antarctic water features from Overture 'water' and 'bathymetry' themes."""
    log.info("Fetching Overture water data …")
    water = geodataframe("water", bbox=ANT_BBOX, stac=True)
    mask = water.geometry.y < -60.0
    water = water[mask].copy()
    log.info("  %d water features south of -60°", len(water))

    water = water.to_crs(OUT_CRS)
    water["water_source"] = "base.water"
    # Rename columns for output
    water = water.rename(
        columns={
            "id": "water_id",
            "subtype": "water_subtype",
        }
    )
    # Get water names
    water_names = []
    for _, row in water.iterrows():
        names = row.get("names")
        if isinstance(names, dict):
            water_names.append(names.get("primary", ""))
        else:
            water_names.append("")
    water["water_name"] = water_names
    water = water[["water_id", "water_name", "water_subtype", "water_source", "geometry"]]

    log.info("Fetching Overture bathymetry data …")
    bathy = geodataframe("bathymetry", bbox=ANT_BBOX, stac=True)
    mask = bathy.geometry.y < -60.0
    bathy = bathy[mask].copy()
    log.info("  %d bathymetry features south of -60°", len(bathy))

    bathy = bathy.to_crs(OUT_CRS)
    bathy["water_source"] = "base.bathymetry"
    bathy["water_id"] = bathy["id"]
    bathy["water_subtype"] = "bathymetry"
    bathy_names = []
    for _, row in bathy.iterrows():
        names = row.get("names")
        if isinstance(names, dict):
            bathy_names.append(names.get("primary", ""))
        else:
            bathy_names.append("")
    bathy["water_name"] = bathy_names
    bathy = bathy[["water_id", "water_name", "water_subtype", "water_source", "geometry"]]

    # Combine
    water_all = pd.concat([water, bathy], ignore_index=True)
    log.info("  Total water+bathymetry features: %d", len(water_all))

    return water_all


# =============================================================================
# 3.  Build 200 km buffer zones, clip to land
# =============================================================================


def build_buffers(stations, land_union):
    """Build 200 km buffers, clip to land."""
    log.info("Building 200 km buffers and clipping to land …")

    station_gdf = gpd.GeoDataFrame(
        stations, geometry="geometry", crs="EPSG:4326"
    ).to_crs(OUT_CRS)

    # Buffer
    station_gdf["buffer"] = station_gdf.geometry.buffer(BUFFER_M, resolution=64)

    land_union_projected = land_union

    # Land-clipped zones: intersection of buffer with land
    clipped = []
    for _, row in station_gdf.iterrows():
        buf = row["buffer"]
        if land_union_projected is None or land_union_projected.is_empty:
            clipped.append(None)
        else:
            inter = buf.intersection(land_union_projected)
            if inter.is_empty:
                clipped.append(None)
            else:
                # Ensure MultiPolygon
                if inter.geom_type == "Polygon":
                    inter = MultiPolygon([inter])
                elif inter.geom_type == "MultiPolygon":
                    pass
                elif inter.geom_type == "GeometryCollection":
                    # Extract only polygons
                    polys = [g for g in inter.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
                    if not polys:
                        clipped.append(None)
                        continue
                    inter = unary_union(polys)
                    if inter.geom_type == "Polygon":
                        inter = MultiPolygon([inter])
                    elif inter.geom_type != "MultiPolygon":
                        clipped.append(None)
                        continue
                else:
                    clipped.append(None)
                    continue
                clipped.append(inter)

    station_gdf["clipped_land"] = clipped
    # Drop rows with no land intersection
    station_gdf = station_gdf[station_gdf["clipped_land"].notna()].copy()
    log.info("  %d stations have non-empty land intersection", len(station_gdf))

    return station_gdf


# =============================================================================
# 4.  Compute coalition groups (overlapping buffers on land)
# =============================================================================


def compute_coalitions(station_gdf):
    """Group overlapping station land polygons into coalitions."""
    log.info("Computing coalition groups …")

    # Spatial join on self to find overlaps
    geoms = station_gdf["clipped_land"].values
    n = len(geoms)

    # Build overlap graph
    adj = {i: set() for i in range(n)}
    for i in range(n):
        for j in range(i + 1, n):
            if geoms[i] is not None and geoms[j] is not None:
                if geoms[i].intersects(geoms[j]):
                    adj[i].add(j)
                    adj[j].add(i)

    # Connected components (BFS)
    visited = set()
    coalitions = {}
    coalition_id = 0
    for i in range(n):
        if i in visited:
            continue
        # BFS
        queue = [i]
        component = set()
        while queue:
            node = queue.pop(0)
            if node in visited:
                continue
            visited.add(node)
            component.add(node)
            for nb in adj[node]:
                if nb not in visited:
                    queue.append(nb)
        for node in component:
            coalitions[node] = coalition_id
        coalition_id += 1

    station_gdf["coalition"] = station_gdf.index.to_series().map(
        lambda idx: coalitions.get(station_gdf.index.get_loc(idx), -1)
    )

    log.info("  Found %d coalitions", station_gdf["coalition"].nunique())
    log.info("  Coalition sizes: %s", station_gdf["coalition"].value_counts().to_dict())

    return station_gdf


# =============================================================================
# 5.  Build output 1: land-clipped coalition zones (dissolved per coalition)
# =============================================================================


def build_coalition_output(station_gdf):
    """Dissolve the station zones by coalition id."""
    log.info("Building output 1: station_spheres …")

    # For each station, we need a row with coalition, station_id, station_name,
    # and the individual clipped geometry (not dissolved).
    # The spec says grouped into coalitions wherever they overlap.
    # "Each coalition gets an integer coalition id"
    # "Every feature needs station_id and station_name"
    # This means each row is a station's clipped zone, attributed with the coalition id.

    out = gpd.GeoDataFrame(
        {
            "coalition": station_gdf["coalition"].astype(int),
            "station_id": station_gdf["station_id"],
            "station_name": station_gdf["station_name"],
        },
        geometry=station_gdf["clipped_land"].values,
        crs=OUT_CRS,
    )

    # Ensure MultiPolygon geometry type
    out["geometry"] = out.geometry.apply(
        lambda g: MultiPolygon([g]) if g.geom_type == "Polygon" else g
    )

    log.info("  Output has %d rows", len(out))
    log.info("  Coalition values: %s", sorted(out["coalition"].unique()))

    return out


# =============================================================================
# 6.  Build output 2: over-water intersections
# =============================================================================


def build_water_output(station_gdf, water_all):
    """Compute over-water portion of each station's buffer and attribute with water features."""
    log.info("Building output 2: station_water_overlap …")

    # Compute the full buffer (before land clip)
    # We need the ocean portion: buffer minus land
    land_union_projected = station_gdf["clipped_land"].iloc[0].__class__  # not used directly
    # Recompute land union
    from shapely.ops import unary_union
    land_union = None
    # Actually, let's compute the ocean portion as: buffer minus land
    # We'll reload the land union or compute from all clipped
    # Actually we still have the land_union from earlier.
    # But we stored it in the global - let's pass it properly.

    rows = []
    for _, row in station_gdf.iterrows():
        buf = row["buffer"]
        # Over-water portion: buffer minus land
        # We already have clipped_land = buf ∩ land
        # So ocean = buf - land = buf.difference(clipped_land)
        ocean = buf.difference(row["clipped_land"])

        if ocean.is_empty:
            continue

        if ocean.geom_type == "GeometryCollection":
            polys = [g for g in ocean.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
            if not polys:
                continue
            ocean = unary_union(polys)
        if ocean.geom_type == "Polygon":
            ocean = MultiPolygon([ocean])
        elif ocean.geom_type != "MultiPolygon":
            continue

        # Find intersecting water features
        water_sub = water_all[water_all.intersects(ocean)].copy()
        if len(water_sub) == 0:
            # Still include even if no water feature - the water sub is the ocean itself?
            # Actually, if there's ocean but no water feature intersects, we might still
            # want to include it. The spec says "over-water portion of each station's zone,
            # attributed with the water features it intersects" - if no water features,
            # we could skip or include with nulls.
            # Let's include with null water attribution
            rows.append(
                {
                    "station_id": row["station_id"],
                    "station_name": row["station_name"],
                    "water_id": None,
                    "water_name": None,
                    "water_subtype": None,
                    "water_source": None,
                    "geometry": ocean,
                }
            )
        else:
            for _, wrow in water_sub.iterrows():
                # Intersection between ocean portion and this water feature
                inter = ocean.intersection(wrow.geometry)
                if inter.is_empty:
                    continue
                if inter.geom_type == "GeometryCollection":
                    polys = [g for g in inter.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
                    if not polys:
                        continue
                    inter = unary_union(polys)
                if inter.geom_type == "Polygon":
                    inter = MultiPolygon([inter])
                elif inter.geom_type != "MultiPolygon":
                    continue

                rows.append(
                    {
                        "station_id": row["station_id"],
                        "station_name": row["station_name"],
                        "water_id": wrow["water_id"],
                        "water_name": wrow["water_name"],
                        "water_subtype": wrow["water_subtype"],
                        "water_source": wrow["water_source"],
                        "geometry": inter,
                    }
                )

    out = gpd.GeoDataFrame(rows, crs=OUT_CRS)
    log.info("  Output has %d rows", len(out))

    return out


# =============================================================================
# 7.  Main
# =============================================================================


def main():
    # Step 1: Stations
    stations = fetch_stations()
    if not stations:
        log.error("No stations found. Aborting.")
        sys.exit(1)

    # Step 2: Land
    land_union = fetch_land()

    # Step 3: Water
    water_all = fetch_water()

    # Step 4: Build buffers clipped to land
    station_gdf = build_buffers(stations, land_union)
    if len(station_gdf) == 0:
        log.error("No stations have non-empty land intersection. Aborting.")
        sys.exit(1)

    # Step 5: Compute coalitions
    station_gdf = compute_coalitions(station_gdf)

    # Step 6: Build output 1
    out1 = build_coalition_output(station_gdf)
    out1.to_parquet("/work/station_spheres.geoparquet")
    log.info("Wrote /work/station_spheres.geoparquet (%d rows)", len(out1))

    # Step 7: Build output 2
    out2 = build_water_output(station_gdf, water_all)
    out2.to_parquet("/work/station_water_overlap.geoparquet")
    log.info("Wrote /work/station_water_overlap.geoparquet (%d rows)", len(out2))

    log.info("Done.")


if __name__ == "__main__":
    main()

Wrote /work/solve.py (20130 chars).

15:46

Let me run this and see how it goes. There's a potential issue: I need to pass the land_union to the build_water_output function, and I need to make sure the buffer column is preserved. Let me review the code and fix the issue with `land_union` in `build_water_output`.

Actually, looking at the code more carefully, in `build_water_output` I do reference `row["buffer"]` but I stored it in `station_gdf`. Let me trace through the flow:

1. `build_buffers` creates station_gdf with 'buffer' column
2. `compute_coalitions` adds 'coalition' column
3. `build_coalition_output` uses station_gdf 
4. `build_water_output` uses station_gdf (which still has 'buffer' and 'clipped_land')

But in `build_water_output` I have a line `land_union_projected = station_gdf["clipped_land"].iloc[0].__class__` which is unused. Let me remove that.

Also I reference `row["buffer"]` in `build_water_output` which should still be in station_gdf since we never remove it. Good.

Let me just run it and fix any issues.

15:46

Bash cd /work && python solve.py 2>&1

2026-06-16 19:18:53,987 INFO Fetching Overture places south of -60° …
2026-06-16 19:19:16,757 INFO   2348 place features south of -60°
2026-06-16 19:19:16,827 INFO Fetching Overture division_area …
2026-06-16 19:19:27,056 INFO   Found 102 unique stations
2026-06-16 19:19:27,057 INFO     AKS Manufactory  (-97.7976, -61.5202)
2026-06-16 19:19:27,057 INFO     Alessandra Oliveira corretora de imóveis rj  (-18.8793, -61.4396)
2026-06-16 19:19:27,057 INFO     Antarctic Kunlun Station  (77.1161, -80.4169)
2026-06-16 19:19:27,057 INFO     Antarctic Plateau  (150.0000, -77.0000)
2026-06-16 19:19:27,057 INFO     Antarctica/Troll  (2.5350, -72.0114)
2026-06-16 19:19:27,057 INFO     Ashford Self Storage Hull Ltd  (40.7812, -76.8408)
2026-06-16 19:19:27,057 INFO     BOSS Menswear Store  (36.1071, -86.8161)
2026-06-16 19:19:27,057 INFO     Babul Yaman Store  (-17.7061, -82.0149)
2026-06-16 19:19:27,057 INFO     Bandith Card Store  (112.5000, -72.6176)
2026-06-16 19:19:27,057 INFO     Base Antártica Marambio  (-56.6235, -64.2374)
2026-06-16 19:19:27,057 INFO     Base Esperanza, Antartida Argentina  (-56.9974, -63.3980)
2026-06-16 19:19:27,057 INFO     Base Marambio, Antartida Argentina  (-56.6198, -64.2413)
2026-06-16 19:19:27,057 INFO     Base McMurdo  (166.6667, -77.8500)
2026-06-16 19:19:27,057 INFO     Base antarctique Concordia  (123.5580, -75.1000)
2026-06-16 19:19:27,057 INFO     Base antarctique Dumont-d'Urville  (140.0013, -66.6628)
2026-06-16 19:19:27,057 INFO     Base antarctique Mawson  (62.8736, -67.6026)
2026-06-16 19:19:27,057 INFO     Base antarctique McMurdo  (166.6667, -77.8500)
2026-06-16 19:19:27,057 INFO     Base antártica Concordia  (123.3333, -75.1000)
2026-06-16 19:19:27,057 INFO     Bellingshausen Russian Antarctic Station  (-58.9624, -62.1998)
2026-06-16 19:19:27,057 INFO     British Antarctic Territory  (-50.0000, -75.0000)
2026-06-16 19:19:27,057 INFO     Brown Station  (-62.8700, -64.8950)
2026-06-16 19:19:27,057 INFO     Brown Station, Antarctic Peninsula  (-62.8724, -64.8921)
2026-06-16 19:19:27,057 INFO     COFFE Station Old town  (116.7188, -84.6078)
2026-06-16 19:19:27,057 INFO     Canoon Store  (-81.8109, -71.0228)
2026-06-16 19:19:27,057 INFO     Carlini Base  (-58.6668, -62.2379)
2026-06-16 19:19:27,057 INFO     Carrefour Hiper Recife Torre  (-34.9103, -80.4079)
2026-06-16 19:19:27,057 INFO     Chittagong Victory National School-CVNS  (-175.3833, -80.4166)
2026-06-16 19:19:27,057 INFO     Concretiza Lar Construtora E Incorporadora  (-59.9706, -63.0673)
2026-06-16 19:19:27,057 INFO     Consultoría Psicológica Especializada de Monclova Psic. Aarón Hernández  (-40.4187, -82.5591)
2026-06-16 19:19:27,057 INFO     Dani Motors  (14.4141, -60.0484)
2026-06-16 19:19:27,058 INFO     Delta Power Generators  (11.7967, -64.0776)
2026-06-16 19:19:27,058 INFO     Discount Hardware Store  (-118.8281, -74.9594)
2026-06-16 19:19:27,058 INFO     EEC Paroisse Plateau Pointe-Noire1  (62.5314, -74.3470)
2026-06-16 19:19:27,058 INFO     Esperanza Base, Antarctica  (-56.9961, -63.3977)
2026-06-16 19:19:27,058 INFO     Everything Store  (-100.1338, -75.8400)
2026-06-16 19:19:27,058 INFO     Extra Space Storage  (38.8842, -77.0941)
2026-06-16 19:19:27,058 INFO     Famous Forum of Worldwide Collaborators LLP  (-26.4430, -68.5652)
2026-06-16 19:19:27,058 INFO     Forland KpK Motors  (57.9277, -72.3400)
2026-06-16 19:19:27,058 INFO     Fuel Tech Micro Gas Station & Dispensers  (15.4688, -65.3393)
2026-06-16 19:19:27,058 INFO     Generika Drugstore - ZDN Bagting Dapitan Page  (-42.7094, -77.6177)
2026-06-16 19:19:27,058 INFO     González Videla Antarctic Base  (-62.8575, -64.8234)
2026-06-16 19:19:27,058 INFO     Good Mates Removals & Storage  (123.9815, -63.4702)
2026-06-16 19:19:27,058 INFO     Grace-Care Laboratory Services and Supplies  (-0.4839, -78.4089)
2026-06-16 19:19:27,058 INFO     HomeVestor PH  (43.0545, -82.7436)
2026-06-16 19:19:27,058 INFO     In Aeternum Historia  (-94.9229, -84.1257)
2026-06-16 19:19:27,058 INFO     JRMSU Katipunan Campus CTEd-Laboratory High School  (123.5915, -69.1798)
2026-06-16 19:19:27,058 INFO     Javastasia Distributors Halaal Meats  (-30.9375, -61.6064)
2026-06-16 19:19:27,058 INFO     Jj'sanm Music Tutorial Center  (18.5354, -70.0807)
2026-06-16 19:19:27,058 INFO     Kimpich Store  (170.5701, -72.9420)
2026-06-16 19:19:27,058 INFO     Kimstore  (118.8281, -68.9110)
2026-06-16 19:19:27,058 INFO     King World Product Store  (29.7141, -60.4919)
2026-06-16 19:19:27,058 INFO     La Combo Tortuga  (-40.7812, -64.4728)
2026-06-16 19:19:27,058 INFO     Lake Vostok  (106.0000, -77.5000)
2026-06-16 19:19:27,058 INFO     Lihor Store Fan  (-62.0934, -66.3728)
2026-06-16 19:19:27,058 INFO     Lori Makiya, Realtor  (80.5643, -63.1742)
2026-06-16 19:19:27,058 INFO     Marambio Base  (-56.6268, -64.2410)
2026-06-16 19:19:27,058 INFO     Mario-Zucchelli-Station  (164.1125, -74.6943)
2026-06-16 19:19:27,058 INFO     Master Bees Stationeries & Souvenirs  (-66.7969, -75.1408)
2026-06-16 19:19:27,058 INFO     McMurdo General Hospital  (166.6693, -77.8468)
2026-06-16 19:19:27,058 INFO     Me_2 Store  (88.2532, -62.3139)
2026-06-16 19:19:27,058 INFO     Melissa Isaac Realtor  (83.2983, -64.2389)
2026-06-16 19:19:27,058 INFO     Mitra Fajar Ciawi " Bengkel Resmi Sepeda Motor Honda "  (-87.6522, -76.1849)
2026-06-16 19:19:27,058 INFO     N'Zemmula - ristorante siciliano  (66.4453, -68.8873)
2026-06-16 19:19:27,058 INFO     N9store.ro  (-42.5968, -63.4702)
2026-06-16 19:19:27,058 INFO     Nadhi/நதி - The Online Saree Store  (151.8750, -80.2502)
2026-06-16 19:19:27,059 INFO     Nadia Pereira - Corretora de Imóveis  (27.5568, -64.9731)
2026-06-16 19:19:27,059 INFO     Nails By Hilary Torres M  (-28.9251, -69.0986)
2026-06-16 19:19:27,059 INFO     Neumayer-Station II  (-8.2500, -70.6500)
2026-06-16 19:19:27,059 INFO     New Lucena Municipal Police Station  (-61.3477, -80.8717)
2026-06-16 19:19:27,059 INFO     Nissan Pampanga by Maica Torres  (57.1124, -63.8013)
2026-06-16 19:19:27,059 INFO     North Store  (164.1299, -70.0807)
2026-06-16 19:19:27,059 INFO     Nueva Ecija Doctors Hospital  (73.1013, -81.4142)
2026-06-16 19:19:27,059 INFO     Palmer Station, Antarctica  (-64.0531, -64.7743)
2026-06-16 19:19:27,059 INFO     Pastor Jorge  (-21.4893, -73.2722)
2026-06-16 19:19:27,059 INFO     Pioneer - Gas Station  (43.9264, -78.9098)
2026-06-16 19:19:27,059 INFO     Pizza Salvatoré  (46.5485, -75.5072)
2026-06-16 19:19:27,059 INFO     Plateau Station  (40.5000, -79.2500)
2026-06-16 19:19:27,059 INFO     Polární stanice McMurdo  (166.6667, -77.8500)
2026-06-16 19:19:27,059 INFO     RT Power Motor  (29.8849, -67.6134)
2026-06-16 19:19:27,059 INFO     Reis Motors  (0.5273, -75.0369)
2026-06-16 19:19:27,059 INFO     Robin Rolon, Mary Kay Independent Sales Director  (-16.0130, -64.9036)
2026-06-16 19:19:27,059 INFO     Rothera Research Station  (-68.1235, -67.5690)
2026-06-16 19:19:27,059 INFO     SANAE  (2.8500, -72.6667)
2026-06-16 19:19:27,059 INFO     Sales Store  (169.8047, -61.8769)
2026-06-16 19:19:27,059 INFO     Storybook Imaging  (-5.6250, -65.6583)
2026-06-16 19:19:27,059 INFO     Sunstar Motor Jepara  (127.6172, -73.1861)
2026-06-16 19:19:27,059 INFO     Suzuki Nilphamari - Lahin Motors  (-121.3709, -65.7744)
2026-06-16 19:19:27,059 INFO     T-Store  (98.7891, -78.0165)
2026-06-16 19:19:27,059 INFO     Technology Store  (-11.2557, -61.2725)
2026-06-16 19:19:27,059 INFO     Timsun Franchise Bayan Lepas - West Motor Racing Centre  (17.3689, -77.7784)
2026-06-16 19:19:27,059 INFO     Toronto Cosmetic Surgery Institute  (-79.3815, -79.3815)
2026-06-16 19:19:27,059 INFO     Tractor Zetor  (50.6250, -76.0161)
2026-06-16 19:19:27,059 INFO     Troll Airfield  (2.4667, -71.9572)
2026-06-16 19:19:27,059 INFO     Ténor store  (66.8828, -61.8563)
2026-06-16 19:19:27,059 INFO     Victors motor driving school  (98.5670, -67.8553)
2026-06-16 19:19:27,059 INFO     Vostok İstasyonu  (106.8373, -78.4644)
2026-06-16 19:19:27,059 INFO     Vumeer el Mentor  (-13.8102, -67.2195)
2026-06-16 19:19:27,059 INFO     Windfreight Express Total Logistics Inc.(WETLI)  (43.0664, -75.0730)
2026-06-16 19:19:27,059 INFO     World Store  (52.8013, -67.4755)
2026-06-16 19:19:27,059 INFO     Yamu Store III  (1.4062, -84.5414)
2026-06-16 19:19:27,060 INFO     شركة يوني فليكس للمعدات والمستلزمات الطبية - Uniflex Medical Supply Store  (-75.2316, -65.2936)
2026-06-16 19:19:27,060 INFO     ร้านข้าวแกงฮาริษา Harisa's Curries Rice Factory  (-65.2176, -70.0806)
2026-06-16 19:19:27,068 INFO Fetching Overture land data …
Traceback (most recent call last):
  File "/work/solve.py", line 595, in <module>
    main()
    ~~~~^^
  File "/work/solve.py", line 567, in main
    land_union = fetch_land()
  File "/work/solve.py", line 239, in fetch_land
    mask = land.geometry.y < -60.0
           ^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/geopandas/geoseries.py", line 301, in y
    return _delegate_property("y", self)
  File "/usr/local/lib/python3.14/site-packages/geopandas/base.py", line 93, in _delegate_property
    data = getattr(a_this, op)
  File "/usr/local/lib/python3.14/site-packages/geopandas/array.py", line 1251, in y
    raise ValueError(message)
ValueError: y attribute access only provided for Point geometries

17:03

The issue is that land geometries are polygons, not points. I need to use the centroid or representative point for the y-coordinate filter. Let me fix this.

17:03

Read /work/solve.py

   226	
   227	
   228	# =============================================================================
   229	# 2.  Fetch Antarctic landmass and water features
   230	# =============================================================================
   231	
   232	
   233	def fetch_land():
   234	    """Fetch Antarctic land polygons from Overture 'land' theme."""
   235	    log.info("Fetching Overture land data …")
   236	    land = geodataframe("land", bbox=ANT_BBOX, stac=True)
   237	
   238	    # Filter to keep only south of -60
   239	    mask = land.geometry.y < -60.0
   240	    land = land[mask].copy()
   241	    log.info("  %d land features south of -60°", len(land))
   242	
   243	    # Reproject to OUT_CRS
   244	    land = land.to_crs(OUT_CRS)
   245	
   246	    # Dissolve into a single land polygon (or multi-polygon)
   247	    land_union = unary_union(land.geometry.values)
   248	    log.info("  Land union done")
   249	
   250	    return land_union
   251	
   252	
   253	def fetch_water():
   254	    """Fetch Antarctic water features from Overture 'water' and 'bathymetry' themes."""
   255	    log.info("Fetching Overture water data …")
   256	    water = geodataframe("water", bbox=ANT_BBOX, stac=True)
   257	    mask = water.geometry.y < -60.0
   258	    water = water[mask].copy()
   259	    log.info("  %d water features south of -60°", len(water))
   260	
   261	    water = water.to_crs(OUT_CRS)
   262	    water["water_source"] = "base.water"
   263	    # Rename columns for output
   264	    water = water.rename(
   265	        columns={
   266	            "id": "water_id",
   267	            "subtype": "water_subtype",
   268	        }
   269	    )
   270	    # Get water names
   271	    water_names = []
   272	    for _, row in water.iterrows():
   273	        names = row.get("names")
   274	        if isinstance(names, dict):
   275	            water_names.append(names.get("primary", ""))
<system-reminder>Showed lines 226-275 of 595. Re-call Read with offset=276 to continue, or raise limit.</system-reminder>

17:23

The issue is that `land.geometry.y` only works for Point geometries, but land features are polygons. I need to use `.centroid.y` or `.representative_point().y` or use the bounds/centroid to filter. Let me fix the filter approach to use the geometry bounds instead.

17:23

Edit /work/solve.py

Edited /work/solve.py.

18:39

Also need to fix the water fetch function to use centroid filter too.

18:39

Edit /work/solve.py

Edited /work/solve.py.

18:51

Edit /work/solve.py

Edited /work/solve.py.

19:26

I also notice the station matching is too aggressive. We're getting many false positives because we're matching on the "base" keyword in lots of unrelated names. Let me also fix the build_water_output function - it needs access to land_union. Let me also fix the station keyword matching to be more selective.

Let me also check issue with the `build_water_output` function. It tries to access `land_union` from `station_gdf["clipped_land"].iloc[0].__class__` which is wrong. Let me fix the function signature to accept land_union.

19:26

Edit /work/solve.py

Edited /work/solve.py.

20:15

Wait, the keyword matching is still going to be too broad. "brown station" won't match "Brown Station, Antarctic Peninsula" (comma) or just "Brown Station". Let me reconsider the approach.

Also "plateau station" could match things with just "plateau" in the name which could be false positives. And "palmer station" won't match "Palmer Station, Antarctica" (comma) or "Palmer Station, Antarctica".

Let me rethink. Instead of keyword fragments, I should use a more targeted approach - collect a known list of Antarctic station names and match against those. Or use the `educational_research_institute` category plus names containing station/base.

Let me take a different approach - create an explicit list of known Antarctic station name patterns and use them more carefully.

20:15

Write /work/solve.py

content · 509 lines · py

#!/usr/bin/env python3
"""
Cross-station logistics-overlap maps for Antarctic Treaty consultative meeting.

Produces two GeoParquet outputs in EPSG:3031 (Antarctic Polar Stereographic):
  1. station_spheres.geoparquet   – land-clipped station zones, grouped into
     coalitions wherever they overlap.  Columns: coalition (int), station_id,
     station_name, geometry (MultiPolygon).
  2. station_water_overlap.geoparquet – over-water portions of each station's
     200 km buffer, attributed with water features intersected.  Columns:
     station_id, station_name, water_id, water_name, water_subtype,
     water_source, geometry (MultiPolygon).
"""

import logging
import sys

import geopandas as gpd
import numpy as np
import pandas as pd
from overturemaps.core import geodataframe
from shapely.geometry import MultiPolygon, Point, Polygon
from shapely.ops import unary_union

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
log = logging.getLogger(__name__)

# ── Antarctic bounding box (lon_min, lat_min, lon_max, lat_max) ──────────────
ANT_BBOX = (-180.0, -90.0, 180.0, -60.0)

# ── Output CRS (Antarctic Polar Stereographic) ───────────────────────────────
OUT_CRS = "EPSG:3031"

# ── Buffer radius in metres ──────────────────────────────────────────────────
BUFFER_M = 200_000  # 200 km

# =============================================================================
#  Known Antarctic research stations  (manually curated)
#  Coordinates from COMNAP / common knowledge
# =============================================================================

# Each entry: (name used for display, Overture-name-keyword to search)
# We search primary names for these substrings (case-insensitive).
STATION_SEARCH_TERMS = [
    "mcmurdo",
    "amundsen-scott",
    "south pole",
    "scott base",
    "mawson",
    "davis station",
    "casey station",
    "halley",
    "rothera",
    "san martin",
    "progress",
    "novolazarevskaya",
    "vostok",
    "concordia",
    "kunlun",
    "troll",
    "tor",
    "neumayer",
    "kohnen",
    "princess elisabeth",
    "syowa",
    "dumont",
    "mirny",
    "bellingshausen",
    "great wall",
    "zhongshan",
    "taishan",
    "jang bogo",
    "king sejong",
    "mario zucchelli",
    "vernadsky",
    "carlini",
    "esperanza",
    "marambio",
    "orcadas",
    "brown station",
    "palmer station",
    "sanae",
    "maitri",
    "bharati",
    "ferraz",
    "ohiggins",
    "frei",
    "escudero",
    "artigas",
    "prat",
    "gabriel de castilla",
    "juan carlos i",
    "melchior",
    "belgrano",
    "matienzo",
    "decepcion",
    "petrel",
    "camara",
    "primavera",
    "teniente",
    "gonzalez videla",
    "gonzález videla",
    "plateau station",
    "byrd station",
    "siple station",
    "molodezhnaya",
    "leningradskaya",
    "mizuho",
    "asuka",
    "sanae",
]


def fetch_stations():
    """Fetch Antarctic research station locations from Overture data."""
    log.info("Fetching Overture places south of -60° …")
    places = geodataframe("place", bbox=ANT_BBOX, stac=True)

    mask = places.geometry.y < -60.0
    places = places[mask].copy()
    log.info("  %d place features south of -60°", len(places))

    stations = []
    matched_ids = set()

    for _, row in places.iterrows():
        names = row.get("names")
        if not isinstance(names, dict):
            continue
        name = names.get("primary", "")
        if not name:
            continue
        name_lower = name.lower()
        if row.geometry.y >= -60.0:
            continue

        # Check search terms
        for term in STATION_SEARCH_TERMS:
            if term in name_lower:
                stations.append(
                    {
                        "station_id": row["id"],
                        "station_name": name,
                        "geometry": row.geometry,
                        "lat": row.geometry.y,
                        "lon": row.geometry.x,
                    }
                )
                matched_ids.add(row["id"])
                break

    # Also check division_area
    log.info("Fetching Overture division_area …")
    div = geodataframe("division_area", bbox=ANT_BBOX, stac=True)
    for _, row in div.iterrows():
        names = row.get("names")
        if not isinstance(names, dict):
            continue
        name = names.get("primary", "")
        if not name:
            continue
        if row["id"] in matched_ids:
            continue
        name_lower = name.lower()
        # Check for station-related division_area entries
        for term in STATION_SEARCH_TERMS:
            if term in name_lower:
                centroid = row.geometry.centroid
                if centroid.y < -60:
                    stations.append(
                        {
                            "station_id": row["id"],
                            "station_name": name,
                            "geometry": centroid,
                            "lat": centroid.y,
                            "lon": centroid.x,
                        }
                    )
                    matched_ids.add(row["id"])
                    break

    # Deduplicate by station_name (case-insensitive)
    seen = set()
    unique = []
    for s in stations:
        key = s["station_name"].lower().strip()
        if key not in seen:
            seen.add(key)
            unique.append(s)

    log.info("  Found %d unique stations", len(unique))
    for s in sorted(unique, key=lambda x: x["station_name"]):
        log.info("    %s  (%.4f, %.4f)", s["station_name"], s["lon"], s["lat"])

    return unique


# =============================================================================
# 2.  Fetch Antarctic landmass and water features
# =============================================================================


def fetch_land():
    """Fetch Antarctic land polygons from Overture 'land' theme."""
    log.info("Fetching Overture land data …")
    land = geodataframe("land", bbox=ANT_BBOX, stac=True)

    mask = land.geometry.centroid.y < -60.0
    land = land[mask].copy()
    log.info("  %d land features south of -60°", len(land))

    land = land.to_crs(OUT_CRS)

    land_union = unary_union(land.geometry.values)
    log.info("  Land union done")

    return land_union


def fetch_water():
    """Fetch Antarctic water features from Overture 'water' and 'bathymetry' themes."""
    log.info("Fetching Overture water data …")
    water = geodataframe("water", bbox=ANT_BBOX, stac=True)
    mask = water.geometry.centroid.y < -60.0
    water = water[mask].copy()
    log.info("  %d water features south of -60°", len(water))

    water = water.to_crs(OUT_CRS)
    water["water_source"] = "base.water"

    # Get names
    def get_name(r):
        n = r.get("names")
        return n.get("primary", "") if isinstance(n, dict) else ""

    water["water_name"] = water.apply(get_name, axis=1)
    water = water.rename(columns={"id": "water_id", "subtype": "water_subtype"})
    water = water[["water_id", "water_name", "water_subtype", "water_source", "geometry"]]

    log.info("Fetching Overture bathymetry data …")
    bathy = geodataframe("bathymetry", bbox=ANT_BBOX, stac=True)
    mask = bathy.geometry.centroid.y < -60.0
    bathy = bathy[mask].copy()
    log.info("  %d bathymetry features south of -60°", len(bathy))

    bathy = bathy.to_crs(OUT_CRS)
    bathy["water_source"] = "base.bathymetry"
    bathy["water_id"] = bathy["id"]
    bathy["water_subtype"] = "bathymetry"
    bathy["water_name"] = bathy.apply(get_name, axis=1)
    bathy = bathy[["water_id", "water_name", "water_subtype", "water_source", "geometry"]]

    water_all = pd.concat([water, bathy], ignore_index=True)
    log.info("  Total water+bathymetry features: %d", len(water_all))

    return water_all


# =============================================================================
# 3.  Build 200 km buffer zones, clip to land
# =============================================================================


def build_buffers(stations, land_union):
    """Build 200 km buffers, clip to land."""
    log.info("Building 200 km buffers and clipping to land …")

    station_gdf = gpd.GeoDataFrame(
        stations, geometry="geometry", crs="EPSG:4326"
    ).to_crs(OUT_CRS)

    # Buffer
    station_gdf["buffer"] = station_gdf.geometry.buffer(BUFFER_M, resolution=64)

    # Land-clipped zones
    clipped = []
    for i, row in station_gdf.iterrows():
        buf = row["buffer"]
        inter = buf.intersection(land_union)
        if inter.is_empty:
            clipped.append(None)
            continue
        # Normalise to MultiPolygon
        if inter.geom_type == "Polygon":
            clipped.append(MultiPolygon([inter]))
        elif inter.geom_type == "MultiPolygon":
            clipped.append(inter)
        elif inter.geom_type == "GeometryCollection":
            polys = [g for g in inter.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
            if not polys:
                clipped.append(None)
                continue
            merged = unary_union(polys)
            if merged.geom_type == "Polygon":
                clipped.append(MultiPolygon([merged]))
            elif merged.geom_type == "MultiPolygon":
                clipped.append(merged)
            else:
                clipped.append(None)
        else:
            clipped.append(None)

    station_gdf["clipped_land"] = clipped
    station_gdf = station_gdf[station_gdf["clipped_land"].notna()].copy()
    log.info("  %d stations have non-empty land intersection", len(station_gdf))

    return station_gdf


# =============================================================================
# 4.  Compute coalition groups (overlapping buffers on land)
# =============================================================================


def compute_coalitions(station_gdf):
    """Group overlapping station land polygons into coalitions."""
    log.info("Computing coalition groups …")

    geoms = station_gdf["clipped_land"].values
    n = len(geoms)

    # Build adjacency via spatial index
    sindex = gpd.GeoSeries(geoms).sindex
    adj = {i: set() for i in range(n)}
    for i in range(n):
        if geoms[i] is None:
            continue
        # Find potential overlaps via bounding box
        possible = list(sindex.intersection(geoms[i].bounds))
        for j in possible:
            if j > i and geoms[j] is not None and geoms[i].intersects(geoms[j]):
                adj[i].add(j)
                adj[j].add(i)

    # Connected components
    visited = set()
    coalition_map = {}
    coalition_id = 0
    for i in range(n):
        if i in visited or geoms[i] is None:
            continue
        queue = [i]
        component = set()
        while queue:
            node = queue.pop(0)
            if node in visited:
                continue
            visited.add(node)
            component.add(node)
            for nb in adj[node]:
                if nb not in visited:
                    queue.append(nb)
        for node in component:
            coalition_map[node] = coalition_id
        coalition_id += 1

    station_gdf["coalition"] = [coalition_map.get(i, -1) for i in range(n)]

    log.info("  Found %d coalitions", station_gdf["coalition"].nunique())
    log.info("  Coalition distribution: %s", station_gdf["coalition"].value_counts().to_dict())

    return station_gdf


# =============================================================================
# 5.  Build output 1: land-clipped coalition zones
# =============================================================================


def build_coalition_output(station_gdf):
    """Build the station_spheres output."""
    log.info("Building output 1: station_spheres …")

    out = gpd.GeoDataFrame(
        {
            "coalition": station_gdf["coalition"].astype(int),
            "station_id": station_gdf["station_id"],
            "station_name": station_gdf["station_name"],
        },
        geometry=station_gdf["clipped_land"].values,
        crs=OUT_CRS,
    )

    log.info("  Output has %d rows", len(out))
    log.info("  Coalition values: %s", sorted(out["coalition"].unique()))

    return out


# =============================================================================
# 6.  Build output 2: over-water intersections
# =============================================================================


def build_water_output(station_gdf, water_all):
    """Compute over-water portion of each station's buffer and attribute with water."""
    log.info("Building output 2: station_water_overlap …")

    rows = []
    for _, row in station_gdf.iterrows():
        buf = row["buffer"]
        land_part = row["clipped_land"]

        # Over-water portion
        ocean = buf.difference(land_part)
        if ocean.is_empty:
            continue

        # Normalise
        if ocean.geom_type == "GeometryCollection":
            polys = [g for g in ocean.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
            if not polys:
                continue
            ocean = unary_union(polys)
        if ocean.geom_type == "Polygon":
            ocean = MultiPolygon([ocean])
        elif ocean.geom_type != "MultiPolygon":
            continue

        # Find intersecting water features
        water_sub = water_all[water_all.intersects(ocean)]
        if len(water_sub) == 0:
            # Ocean without mapped water features – still include
            rows.append(
                {
                    "station_id": row["station_id"],
                    "station_name": row["station_name"],
                    "water_id": None,
                    "water_name": None,
                    "water_subtype": None,
                    "water_source": None,
                    "geometry": ocean,
                }
            )
        else:
            for _, wrow in water_sub.iterrows():
                inter = ocean.intersection(wrow.geometry)
                if inter.is_empty:
                    continue
                if inter.geom_type == "GeometryCollection":
                    polys = [g for g in inter.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
                    if not polys:
                        continue
                    inter = unary_union(polys)
                if inter.geom_type == "Polygon":
                    inter = MultiPolygon([inter])
                elif inter.geom_type != "MultiPolygon":
                    continue

                rows.append(
                    {
                        "station_id": row["station_id"],
                        "station_name": row["station_name"],
                        "water_id": wrow["water_id"],
                        "water_name": wrow["water_name"],
                        "water_subtype": wrow["water_subtype"],
                        "water_source": wrow["water_source"],
                        "geometry": inter,
                    }
                )

    out = gpd.GeoDataFrame(rows, crs=OUT_CRS)
    log.info("  Output has %d rows", len(out))

    return out


# =============================================================================
# 7.  Main
# =============================================================================


def main():
    # Step 1: Stations
    stations = fetch_stations()
    if not stations:
        log.error("No stations found. Aborting.")
        sys.exit(1)

    # Step 2: Land
    land_union = fetch_land()

    # Step 3: Water
    water_all = fetch_water()

    # Step 4: Build buffers clipped to land
    station_gdf = build_buffers(stations, land_union)
    if len(station_gdf) == 0:
        log.error("No stations have non-empty land intersection. Aborting.")
        sys.exit(1)

    # Step 5: Compute coalitions
    station_gdf = compute_coalitions(station_gdf)

    # Step 6: Build output 1
    out1 = build_coalition_output(station_gdf)
    out1.to_parquet("/work/station_spheres.geoparquet")
    log.info("Wrote /work/station_spheres.geoparquet (%d rows)", len(out1))

    # Step 7: Build output 2
    out2 = build_water_output(station_gdf, water_all)
    out2.to_parquet("/work/station_water_overlap.geoparquet")
    log.info("Wrote /work/station_water_overlap.geoparquet (%d rows)", len(out2))

    log.info("Done.")


if __name__ == "__main__":
    main()

Wrote /work/solve.py (16384 chars).

20:41

Let me first check what stations we'll get with the current search terms before running the full script.

20:41

Bash cd /work && python -c "

cd /work && python -c "
from overturemaps.core import geodataframe
bbox = (-180, -90, 180, -60)
places = geodataframe('place', bbox=bbox, stac=True)
mask = places.geometry.y < -60.0
places = places[mask].copy()

search_terms = ['mcmurdo','amundsen-scott','south pole','scott base','mawson','davis station',
    'casey station','halley','rothera','san martin','progress','novolazarevskaya','vostok',
    'concordia','kunlun','troll','tor','neumayer','kohnen','princess elisabeth','syowa',
    'dumont','mirny','bellingshausen','great wall','zhongshan','taishan','jang bogo',
    'king sejong','mario zucchelli','vernadsky','carlini','esperanza','marambio','orcadas',
    'brown station','palmer station','sanae','maitri','bharati','ferraz','ohiggins','frei',
    'escudero','artigas','prat','gabriel de castilla','juan carlos i','melchior','belgrano',
    'matienzo','decepcion','petrel','camara','primavera','teniente','gonzalez videla',
    'gonzález videla','plateau station','byrd station','siple station','molodezhnaya',
    'leningradskaya','mizuho','asuka']

for _, row in places.iterrows():
    names = row.get('names')
    if not isinstance(names, dict):
        continue
    name = names.get('primary', '')
    if not name:
        continue
    nl = name.lower()
    for term in search_terms:
        if term in nl:
            print(f'{name:60s} | ({row.geometry.x:.4f}, {row.geometry.y:.4f}) | match: {term}')
            break
" 2>&1

Chittagong Victory National School-CVNS                      | (-175.3833, -80.4166) | match: tor
Suzuki Nilphamari - Lahin Motors                             | (-121.3709, -65.7744) | match: tor
Discount Hardware Store                                      | (-118.8281, -74.9594) | match: tor
Everything Store                                             | (-100.1338, -75.8400) | match: tor
AKS Manufactory                                              | (-97.7976, -61.5202) | match: tor
In Aeternum Historia                                         | (-94.9229, -84.1257) | match: tor
Mitra Fajar Ciawi " Bengkel Resmi Sepeda Motor Honda "       | (-87.6522, -76.1849) | match: tor
Canoon Store                                                 | (-81.8109, -71.0228) | match: tor
Toronto Cosmetic Surgery Institute                           | (-79.3815, -79.3815) | match: tor
شركة يوني فليكس للمعدات والمستلزمات الطبية - Uniflex Medical Supply Store | (-75.2316, -65.2936) | match: tor
ร้านข้าวแกงฮาริษา Harisa's Curries Rice Factory              | (-65.2176, -70.0806) | match: tor
Lihor Store Fan                                              | (-62.0934, -66.3728) | match: tor
Brown Station, Antarctic Peninsula                           | (-62.8724, -64.8921) | match: brown station
Brown Station                                                | (-62.8700, -64.8950) | match: brown station
González Videla Antarctic Base                               | (-62.8575, -64.8234) | match: gonzález videla
Palmer Station, Antarctica                                   | (-64.0531, -64.7743) | match: palmer station
Carlini Base                                                 | (-58.6668, -62.2379) | match: carlini
Bellingshausen Russian Antarctic Station                     | (-58.9624, -62.1998) | match: bellingshausen
Concretiza Lar Construtora E Incorporadora                   | (-59.9706, -63.0673) | match: tor
Base Marambio, Antartida Argentina                           | (-56.6198, -64.2413) | match: marambio
Marambio Base                                                | (-56.6268, -64.2410) | match: marambio
Base Antártica Marambio                                      | (-56.6235, -64.2374) | match: marambio
Esperanza Base, Antarctica                                   | (-56.9961, -63.3977) | match: esperanza
Base Esperanza, Antartida Argentina                          | (-56.9974, -63.3980) | match: esperanza
British Antarctic Territory                                  | (-50.0000, -75.0000) | match: tor
Consultoría Psicológica Especializada de Monclova Psic. Aarón Hernández | (-40.4187, -82.5591) | match: tor
Generika Drugstore - ZDN Bagting Dapitan Page                | (-42.7094, -77.6177) | match: tor
Carrefour Hiper Recife Torre                                 | (-34.9103, -80.4079) | match: tor
Pastor Jorge                                                 | (-21.4893, -73.2722) | match: tor
Nails By Hilary Torres M                                     | (-28.9251, -69.0986) | match: tor
Famous Forum of Worldwide Collaborators LLP                  | (-26.4430, -68.5652) | match: tor
Javastasia Distributors Halaal Meats                         | (-30.9375, -61.6064) | match: tor
N9store.ro                                                   | (-42.5968, -63.4702) | match: tor
La Combo Tortuga                                             | (-40.7812, -64.4728) | match: tor
Storybook Imaging                                            | (-5.6250, -65.6583) | match: tor
Technology Store                                             | (-11.2557, -61.2725) | match: tor
Alessandra Oliveira corretora de imóveis rj                  | (-18.8793, -61.4396) | match: tor
Robin Rolon, Mary Kay Independent Sales Director             | (-16.0130, -64.9036) | match: tor
Vumeer el Mentor                                             | (-13.8102, -67.2195) | match: tor
Babul Yaman Store                                            | (-17.7061, -82.0149) | match: tor
Neumayer-Station II                                          | (-8.2500, -70.6500) | match: neumayer
Reis Motors                                                  | (0.5273, -75.0369) | match: tor
Troll Airfield                                               | (2.4667, -71.9572) | match: troll
Antarctica/Troll                                             | (2.5350, -72.0114) | match: troll
SANAE                                                        | (2.8500, -72.6667) | match: sanae
Grace-Care Laboratory Services and Supplies                  | (-0.4839, -78.4089) | match: tor
Yamu Store III                                               | (1.4062, -84.5414) | match: tor
Jj'sanm Music Tutorial Center                                | (18.5354, -70.0807) | match: tor
Dani Motors                                                  | (14.4141, -60.0484) | match: tor
Delta Power Generators                                       | (11.7967, -64.0776) | match: tor
Timsun Franchise Bayan Lepas - West Motor Racing Centre      | (17.3689, -77.7784) | match: tor
Nadia Pereira - Corretora de Imóveis                         | (27.5568, -64.9731) | match: tor
RT Power Motor                                               | (29.8849, -67.6134) | match: tor
King World Product Store                                     | (29.7141, -60.4919) | match: tor
BOSS Menswear Store                                          | (36.1071, -86.8161) | match: tor
Extra Space Storage                                          | (38.8842, -77.0941) | match: tor
Extra Space Storage                                          | (38.9075, -77.4239) | match: tor
Ashford Self Storage Hull Ltd                                | (40.7812, -76.8408) | match: tor
Windfreight Express Total Logistics Inc.(WETLI)              | (43.0664, -75.0730) | match: frei
Plateau Station                                              | (40.5000, -79.2500) | match: plateau station
HomeVestor PH                                                | (43.0545, -82.7436) | match: tor
Tractor Zetor                                                | (50.6250, -76.0161) | match: tor
Pizza Salvatoré                                              | (46.5485, -75.5072) | match: tor
Pizza Salvatoré                                              | (48.5721, -78.1316) | match: tor
Pizza Salvatoré                                              | (48.0982, -77.8300) | match: tor
Pizza Salvatoré                                              | (48.8055, -79.2000) | match: tor
Pizza Salvatoré                                              | (48.2268, -79.0067) | match: tor
Pizza Salvatoré                                              | (45.4189, -75.8088) | match: tor
Pizza Salvatoré                                              | (45.4896, -75.6745) | match: tor
Pizza Salvatoré                                              | (45.5534, -75.4362) | match: tor
Pizza Salvatoré                                              | (45.2646, -74.2186) | match: tor
Pizza Salvatoré                                              | (45.2724, -74.1208) | match: tor
Pizza Salvatoré                                              | (45.3081, -73.8604) | match: tor
Pizza Salvatoré                                              | (45.4915, -73.8508) | match: tor
Pizza Salvatoré                                              | (45.4849, -73.7963) | match: tor
Pizza Salvatoré                                              | (45.5280, -73.9428) | match: tor
Pizza Salvatoré                                              | (45.3783, -74.0208) | match: tor
Pizza Salvatoré                                              | (45.6107, -74.6051) | match: tor
Pizza Salvatoré                                              | (45.6563, -74.3313) | match: tor
Pizza Salvatoré                                              | (46.0454, -74.2761) | match: tor
Pizza Salvatoré                                              | (45.9495, -74.1314) | match: tor
Pizza Salvatoré                                              | (45.7659, -73.8122) | match: tor
Pizza Salvatoré                                              | (45.8554, -74.0683) | match: tor
Pizza Salvatoré                                              | (45.8933, -74.1489) | match: tor
Pizza Salvatoré                                              | (45.7961, -74.0090) | match: tor
Pizza Salvatoré                                              | (45.7029, -74.0916) | match: tor
Pizza Salvatoré                                              | (45.6959, -73.9336) | match: tor
Pizza Salvatoré                                              | (45.6396, -73.8278) | match: tor
Pizza Salvatoré                                              | (45.5761, -73.8763) | match: tor
Pizza Salvatoré                                              | (45.5776, -73.6998) | match: tor
Pizza Salvatoré                                              | (45.6107, -73.7354) | match: tor
Pizza Salvatoré                                              | (45.6672, -73.7572) | match: tor
Pizza Salvatoré                                              | (45.7189, -73.6569) | match: tor
Pizza Salvatoré                                              | (45.7429, -73.6100) | match: tor
Pizza Salvatoré                                              | (45.5839, -73.5726) | match: tor
Pizza Salvatoré                                              | (45.5904, -73.4357) | match: tor
Pizza Salvatoré                                              | (45.5843, -73.3261) | match: tor
Pizza Salvatoré                                              | (45.7536, -73.4692) | match: tor
Pizza Salvatoré                                              | (45.6865, -73.4314) | match: tor
Pizza Salvatoré                                              | (45.7589, -73.4255) | match: tor
Pizza Salvatoré                                              | (45.8941, -73.2982) | match: tor
Pizza Salvatoré                                              | (46.0356, -73.7027) | match: tor
Pizza Salvatoré                                              | (46.0395, -73.4462) | match: tor
Pizza Salvatoré                                              | (46.0413, -73.1016) | match: tor
Pizza Salvatoré                                              | (45.6230, -72.9588) | match: tor
Pizza Salvatoré                                              | (45.4149, -73.0165) | match: tor
Pizza Salvatoré                                              | (45.2866, -72.9898) | match: tor
Pizza Salvatoré                                              | (45.3381, -73.2695) | match: tor
Pizza Salvatoré                                              | (45.5555, -73.2131) | match: tor
Pizza Salvatoré                                              | (45.5295, -73.2888) | match: tor
Pizza Salvatoré                                              | (45.4091, -73.4743) | match: tor
Pizza Salvatoré                                              | (45.4996, -73.4862) | match: tor
Pizza Salvatoré                                              | (45.4770, -73.4313) | match: tor
Pizza Salvatoré                                              | (45.5120, -73.4408) | match: tor
Pizza Salvatoré                                              | (45.5384, -73.6991) | match: tor
Pizza Salvatoré                                              | (45.3890, -73.5667) | match: tor
Pizza Salvatoré                                              | (45.2670, -73.6117) | match: tor
Pizza Salvatoré                                              | (45.2762, -72.1301) | match: tor
Pizza Salvatoré                                              | (45.3674, -71.9961) | match: tor
Pizza Salvatoré                                              | (45.3974, -71.9407) | match: tor
Pizza Salvatoré                                              | (45.3762, -71.8926) | match: tor
Pizza Salvatoré                                              | (45.4083, -71.8692) | match: tor
Pizza Salvatoré                                              | (45.2019, -72.7474) | match: tor
Pizza Salvatoré                                              | (45.4138, -72.7533) | match: tor
Pizza Salvatoré                                              | (45.6459, -72.5707) | match: tor
Pizza Salvatoré                                              | (45.8880, -72.5046) | match: tor
Pizza Salvatoré                                              | (46.2766, -72.5109) | match: tor
Pizza Salvatoré                                              | (46.0572, -71.9477) | match: tor
Pizza Salvatoré                                              | (45.7712, -71.9283) | match: tor
Pizza Salvatoré                                              | (46.2154, -71.7680) | match: tor
Pizza Salvatoré                                              | (46.1018, -71.3149) | match: tor
Pizza Salvatoré                                              | (45.1337, -71.8025) | match: tor
Pizza Salvatoré                                              | (45.5898, -70.9033) | match: tor
Pizza Salvatoré                                              | (46.1303, -70.6936) | match: tor
Pizza Salvatoré                                              | (46.2106, -70.7747) | match: tor
Pizza Salvatoré                                              | (46.9811, -70.5664) | match: tor
Pizza Salvatoré                                              | (47.4431, -70.5161) | match: tor
Pizza Salvatoré                                              | (47.3714, -70.0413) | match: tor
Pizza Salvatoré                                              | (47.8267, -69.5480) | match: tor
Pizza Salvatoré                                              | (48.4561, -68.5012) | match: tor
Pizza Salvatoré                                              | (48.5943, -68.2076) | match: tor
Pizza Salvatoré                                              | (48.8527, -67.5111) | match: tor
Pizza Salvatoré                                              | (49.2003, -68.2562) | match: tor
Pizza Salvatoré                                              | (48.8805, -72.2313) | match: tor
Pizza Salvatoré                                              | (48.5234, -72.2305) | match: tor
Pizza Salvatoré                                              | (48.5450, -71.6445) | match: tor
Pizza Salvatoré                                              | (48.4095, -71.2128) | match: tor
Pizza Salvatoré                                              | (48.4517, -71.0943) | match: tor
Pizza Salvatoré                                              | (48.3983, -71.0644) | match: tor
Pizza Salvatoré                                              | (47.0135, -70.9488) | match: tor
Pizza Salvatoré                                              | (46.9120, -71.3342) | match: tor
Pizza Salvatoré                                              | (46.8988, -71.1907) | match: tor
Pizza Salvatoré                                              | (46.8749, -71.1967) | match: tor
Pizza Salvatoré                                              | (46.8382, -71.2455) | match: tor
Pizza Salvatoré                                              | (46.8125, -71.2184) | match: tor
Pizza Salvatoré                                              | (46.8116, -71.2324) | match: tor
Pizza Salvatoré                                              | (46.8705, -71.2769) | match: tor
Pizza Salvatoré                                              | (46.8329, -71.2784) | match: tor
Pizza Salvatoré                                              | (46.7856, -71.2820) | match: tor
Pizza Salvatoré                                              | (46.7941, -71.3511) | match: tor
Pizza Salvatoré                                              | (46.7188, -71.3001) | match: tor
Pizza Salvatoré                                              | (46.7308, -71.2086) | match: tor
Pizza Salvatoré                                              | (46.8155, -71.1489) | match: tor
Pizza Salvatoré                                              | (46.6324, -70.9772) | match: tor
Pizza Salvatoré                                              | (46.4482, -71.0284) | match: tor
Pizza Salvatoré                                              | (46.5375, -71.6367) | match: tor
Pizza Salvatoré                                              | (46.6816, -71.7272) | match: tor
Pizza Salvatoré                                              | (46.8894, -71.8337) | match: tor
Pizza Salvatoré                                              | (46.7486, -71.6944) | match: tor
Pizza Salvatoré                                              | (46.7483, -71.4401) | match: tor
Pizza Salvatoré                                              | (46.8581, -71.4104) | match: tor
Pizza Salvatoré                                              | (46.3708, -72.5557) | match: tor
Pizza Salvatoré                                              | (46.3628, -72.5942) | match: tor
Pizza Salvatoré                                              | (46.3360, -72.5897) | match: tor
Pizza Salvatoré                                              | (46.5501, -72.7469) | match: tor
Pizza Salvatoré                                              | (47.4376, -72.7842) | match: tor
World Store                                                  | (52.8013, -67.4755) | match: tor
Pizza Salvatoré                                              | (50.2175, -66.3819) | match: tor
Pizza Salvatoré                                              | (47.6410, -65.6829) | match: tor
Pizza Salvatoré                                              | (47.0309, -65.4902) | match: tor
Pizza Salvatoré                                              | (46.0865, -64.7342) | match: tor
Pizza Salvatoré                                              | (46.1141, -64.8356) | match: tor
Pizza Salvatoré                                              | (45.9763, -66.6472) | match: tor
Pizza Salvatoré                                              | (45.3093, -66.0155) | match: tor
Pizza Salvatoré                                              | (44.7646, -63.6763) | match: tor
Ténor store                                                  | (66.8828, -61.8563) | match: tor
Nissan Pampanga by Maica Torres                              | (57.1124, -63.8013) | match: tor
Forland KpK Motors                                           | (57.9277, -72.3400) | match: tor
Base antarctique Mawson                                      | (62.8736, -67.6026) | match: mawson
N'Zemmula - ristorante siciliano                             | (66.4453, -68.8873) | match: tor
Nueva Ecija Doctors Hospital                                 | (73.1013, -81.4142) | match: tor
Antarctic Kunlun Station                                     | (77.1161, -80.4169) | match: kunlun
Lori Makiya, Realtor                                         | (80.5643, -63.1742) | match: tor
Melissa Isaac Realtor                                        | (83.2983, -64.2389) | match: tor
Me_2 Store                                                   | (88.2532, -62.3139) | match: tor
Victors motor driving school                                 | (98.5670, -67.8553) | match: tor
T-Store                                                      | (98.7891, -78.0165) | match: tor
Lake Vostok                                                  | (106.0000, -77.5000) | match: vostok
Vostok İstasyonu                                             | (106.8373, -78.4644) | match: vostok
Bandith Card Store                                           | (112.5000, -72.6176) | match: tor
Base antártica Concordia                                     | (123.3333, -75.1000) | match: concordia
Base antarctique Concordia                                   | (123.5580, -75.1000) | match: concordia
JRMSU Katipunan Campus CTEd-Laboratory High School           | (123.5915, -69.1798) | match: tor
Sunstar Motor Jepara                                         | (127.6172, -73.1861) | match: tor
Good Mates Removals & Storage                                | (123.9815, -63.4702) | match: tor
Kimstore                                                     | (118.8281, -68.9110) | match: tor
Base antarctique Dumont-d'Urville                            | (140.0013, -66.6628) | match: dumont
Nadhi/நதி - The Online Saree Store                           | (151.8750, -80.2502) | match: tor
North Store                                                  | (164.1299, -70.0807) | match: tor
McMurdo General Hospital                                     | (166.6693, -77.8468) | match: mcmurdo
Base McMurdo                                                 | (166.6667, -77.8500) | match: mcmurdo
Polární stanice McMurdo                                      | (166.6667, -77.8500) | match: mcmurdo
Base antarctique McMurdo                                     | (166.6667, -77.8500) | match: mcmurdo
Kimpich Store                                                | (170.5701, -72.9420) | match: tor
Sales Store                                                  | (169.8047, -61.8769) | match: tor

21:48

The issue is that "tor" matches "Toronto" and "Victoria" and "Tortuga" etc. I need to fix the search terms. "tor" was intended to match the Norwegian "Tor" station, but it matches too many things. Let me be much more careful with search terms. I should use whole-word matching where possible, and for "Tor" station I need to match it as "Tor station" or similar.

Let me take a different approach: use a curated list of station names that are known to exist in the Overture dataset, and just hard-match those exact names.

21:48

Write /work/solve.py

content · 569 lines · py

#!/usr/bin/env python3
"""
Cross-station logistics-overlap maps for Antarctic Treaty consultative meeting.

Produces two GeoParquet outputs in EPSG:3031 (Antarctic Polar Stereographic):
  1. station_spheres.geoparquet   – land-clipped station zones, grouped into
     coalitions wherever they overlap.  Columns: coalition (int), station_id,
     station_name, geometry (MultiPolygon).
  2. station_water_overlap.geoparquet – over-water portions of each station's
     200 km buffer, attributed with water features intersected.  Columns:
     station_id, station_name, water_id, water_name, water_subtype,
     water_source, geometry (MultiPolygon).
"""

import logging
import re
import sys

import geopandas as gpd
import numpy as np
import pandas as pd
from overturemaps.core import geodataframe
from shapely.geometry import MultiPolygon, Point, Polygon
from shapely.ops import unary_union

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
log = logging.getLogger(__name__)

# ── Antarctic bounding box ──────────────────────────────────
ANT_BBOX = (-180.0, -90.0, 180.0, -60.0)

# ── Output CRS (Antarctic Polar Stereographic) ───────────────
OUT_CRS = "EPSG:3031"

# ── Buffer radius in metres ──────────────────────────────────
BUFFER_M = 200_000

# =============================================================================
#  Known Antarctic research stations – exact primary-name matching
# =============================================================================

# These are the exact primary names (from Overture 'place' or 'division_area')
# that correspond to actual research stations.
KNOWN_STATION_NAMES = frozenset(
    s.casefold()
    for s in [
        # Major stations found in Overture data
        "Base McMurdo",
        "Base antarctique McMurdo",
        "Polární stanice McMurdo",
        "Bellingshausen Russian Antarctic Station",
        "Carlini Base",
        "Esperanza Base, Antarctica",
        "Base Esperanza, Antartida Argentina",
        "Base Marambio, Antartida Argentina",
        "Marambio Base",
        "Base Antártica Marambio",
        "Neumayer-Station II",
        "Base antarctique Mawson",
        "Base antarctique Concordia",
        "Base antártica Concordia",
        "Base antarctique Dumont-d'Urville",
        "Brown Station",
        "Brown Station, Antarctic Peninsula",
        "Palmer Station, Antarctica",
        "Antarctic Kunlun Station",
        "Vostok İstasyonu",
        "Lake Vostok",
        "Plateau Station",
        "Mario-Zucchelli-Station",
        "González Videla Antarctic Base",
        "Troll Airfield",
        "Antarctica/Troll",
        "SANAE",
        "Rothera Research Station",
        "McMurdo General Hospital",
    ]
)

# Additional stations we need to try to get by keywords (very specific)
# These are extra keywords that won't cause false-positives
EXTRA_TERMS = [
    ("rothera", "Rothera Research Station"),
    ("sanae", "SANAE"),
    ("troll", None),  # matched via name
    ("tor", None),    # only valid if we have "Tor" as the station
]

# For terms like "tor", we need word-boundary matching
def _safe_search_terms():
    """Return list of (compiled_regex, display_name_or_None)."""
    terms = []
    # Names that might appear as substrings in other names
    safe_terms = {
        "mcmurdo",
        "amundsen-scott",
        "scott base",
        "mawson",
        "davis station",
        "casey station",
        "halley",
        "rothera",
        "san martin",
        "progress",
        "novolazarevskaya",
        "vostok",
        "concordia",
        "kunlun",
        "troll",
        "tor station",
        "neumayer",
        "kohnen",
        "princess elisabeth",
        "syowa",
        "dumont",
        "mirny",
        "bellingshausen",
        "great wall",
        "zhongshan",
        "taishan",
        "jang bogo",
        "king sejong",
        "mario zucchelli",
        "zucchelli",
        "vernadsky",
        "carlini",
        "esperanza",
        "marambio",
        "orcadas",
        "brown station",
        "palmer station",
        "sanae",
        "maitri",
        "bharati",
        "ferraz",
        "ohiggins",
        "frei",
        "escudero",
        "artigas",
        "prat",
        "gabriel de castilla",
        "juan carlos i",
        "melchior",
        "belgrano",
        "matienzo",
        "decepcion",
        "petrel",
        "camara",
        "primavera",
        "teniente",
        "gonzalez videla",
        "gonzález videla",
        "plateau station",
        "byrd station",
        "siple station",
        "molodezhnaya",
        "leningradskaya",
        "mizuho",
        "asuka",
    }
    for term in safe_terms:
        # Use word-boundary for short terms to avoid substring false positives
        if len(term) <= 5:
            terms.append((re.compile(r"\b" + re.escape(term) + r"\b", re.IGNORECASE), None))
        else:
            terms.append((re.compile(re.escape(term), re.IGNORECASE), None))
    return terms


def fetch_stations():
    """Fetch Antarctic research station locations from Overture data."""
    log.info("Fetching Overture places south of -60° …")
    places = geodataframe("place", bbox=ANT_BBOX, stac=True)

    mask = places.geometry.y < -60.0
    places = places[mask].copy()
    log.info("  %d place features south of -60°", len(places))

    # Pre-compute search patterns
    safe_patterns = _safe_search_terms()

    stations = []
    matched_ids = set()

    def add_if_match(row, station_name_override=None):
        """Check a row against known names and patterns."""
        names = row.get("names")
        if not isinstance(names, dict):
            return False
        name = names.get("primary", "")
        if not name:
            return False
        name_lower = name.casefold()

        # 1) Exact match against KNOWN_STATION_NAMES
        if name_lower in KNOWN_STATION_NAMES:
            stations.append(
                {
                    "station_id": row["id"],
                    "station_name": name,
                    "geometry": row.geometry if hasattr(row.geometry, 'x') else row.geometry.centroid,
                    "lat": (row.geometry.y if hasattr(row.geometry, 'y')
                            else row.geometry.centroid.y),
                    "lon": (row.geometry.x if hasattr(row.geometry, 'x')
                            else row.geometry.centroid.x),
                }
            )
            matched_ids.add(row["id"])
            return True

        # 2) Pattern-based search with safe terms
        for pat, _ in safe_patterns:
            if pat.search(name):
                stations.append(
                    {
                        "station_id": row["id"],
                        "station_name": name,
                        "geometry": row.geometry if hasattr(row.geometry, 'x') else row.geometry.centroid,
                        "lat": (row.geometry.y if hasattr(row.geometry, 'y')
                                else row.geometry.centroid.y),
                        "lon": (row.geometry.x if hasattr(row.geometry, 'x')
                                else row.geometry.centroid.x),
                    }
                )
                matched_ids.add(row["id"])
                return True

        return False

    for _, row in places.iterrows():
        add_if_match(row)

    # Also check division_area
    log.info("Fetching Overture division_area …")
    div = geodataframe("division_area", bbox=ANT_BBOX, stac=True)
    for _, row in div.iterrows():
        names = row.get("names")
        if not isinstance(names, dict):
            continue
        name = names.get("primary", "")
        if not name:
            continue
        if row["id"] in matched_ids:
            continue
        centroid = row.geometry.centroid
        if centroid.y < -60:
            # Temporarily make a Point-like row object
            class RowProxy:
                def __init__(self, id_, names_dict, centroid):
                    self.id = id_
                    self._names = names_dict
                    self.geometry = centroid
                def get(self, k, default=None):
                    return self._names if k == "names" else default

            proxy = RowProxy(row["id"], names, centroid)
            add_if_match(proxy)

    # Deduplicate
    seen = set()
    unique = []
    for s in stations:
        key = s["station_name"].casefold().strip()
        if key not in seen:
            seen.add(key)
            unique.append(s)

    log.info("  Found %d unique stations", len(unique))
    for s in sorted(unique, key=lambda x: x["station_name"]):
        log.info("    %s  (%.4f, %.4f)", s["station_name"], s["lon"], s["lat"])

    return unique


# =============================================================================
# 2.  Fetch Antarctic landmass and water features
# =============================================================================


def fetch_land():
    """Fetch Antarctic land from Overture 'land' theme."""
    log.info("Fetching Overture land data …")
    land = geodataframe("land", bbox=ANT_BBOX, stac=True)

    mask = land.geometry.centroid.y < -60.0
    land = land[mask].copy()
    log.info("  %d land features south of -60°", len(land))

    land = land.to_crs(OUT_CRS)

    land_union = unary_union(land.geometry.values)
    log.info("  Land union done")

    return land_union


def fetch_water():
    """Fetch Antarctic water features from Overture 'water' and 'bathymetry'."""
    log.info("Fetching Overture water data …")
    water = geodataframe("water", bbox=ANT_BBOX, stac=True)
    mask = water.geometry.centroid.y < -60.0
    water = water[mask].copy()
    log.info("  %d water features south of -60°", len(water))

    water = water.to_crs(OUT_CRS)
    water["water_source"] = "base.water"

    def get_name(r):
        n = r.get("names")
        return n.get("primary", "") if isinstance(n, dict) else ""

    water["water_name"] = water.apply(get_name, axis=1)
    water = water.rename(columns={"id": "water_id", "subtype": "water_subtype"})
    water = water[["water_id", "water_name", "water_subtype", "water_source", "geometry"]]

    log.info("Fetching Overture bathymetry data …")
    bathy = geodataframe("bathymetry", bbox=ANT_BBOX, stac=True)
    mask = bathy.geometry.centroid.y < -60.0
    bathy = bathy[mask].copy()
    log.info("  %d bathymetry features south of -60°", len(bathy))

    bathy = bathy.to_crs(OUT_CRS)
    bathy["water_source"] = "base.bathymetry"
    bathy["water_id"] = bathy["id"]
    bathy["water_subtype"] = "bathymetry"
    bathy["water_name"] = bathy.apply(get_name, axis=1)
    bathy = bathy[["water_id", "water_name", "water_subtype", "water_source", "geometry"]]

    water_all = pd.concat([water, bathy], ignore_index=True)
    log.info("  Total water+bathymetry features: %d", len(water_all))

    return water_all


# =============================================================================
# 3.  Build 200 km buffer zones, clip to land
# =============================================================================


def build_buffers(stations, land_union):
    """Build 200 km buffers, clip to land."""
    log.info("Building 200 km buffers and clipping to land …")

    station_gdf = gpd.GeoDataFrame(
        stations, geometry="geometry", crs="EPSG:4326"
    ).to_crs(OUT_CRS)

    station_gdf["buffer"] = station_gdf.geometry.buffer(BUFFER_M, resolution=64)

    def clip_to_land(buf):
        inter = buf.intersection(land_union)
        if inter.is_empty:
            return None
        if inter.geom_type == "Polygon":
            return MultiPolygon([inter])
        elif inter.geom_type == "MultiPolygon":
            return inter
        elif inter.geom_type == "GeometryCollection":
            polys = [g for g in inter.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
            if not polys:
                return None
            merged = unary_union(polys)
            if merged.geom_type == "Polygon":
                return MultiPolygon([merged])
            elif merged.geom_type == "MultiPolygon":
                return merged
        return None

    station_gdf["clipped_land"] = station_gdf["buffer"].apply(clip_to_land)
    station_gdf = station_gdf[station_gdf["clipped_land"].notna()].copy()
    log.info("  %d stations have non-empty land intersection", len(station_gdf))

    return station_gdf


# =============================================================================
# 4.  Compute coalition groups (overlapping buffers on land)
# =============================================================================


def compute_coalitions(station_gdf):
    """Group overlapping station land polygons into coalitions."""
    log.info("Computing coalition groups …")

    geoms = station_gdf["clipped_land"].values
    n = len(geoms)
    if n == 0:
        station_gdf["coalition"] = []
        return station_gdf

    # Build adjacency via spatial index
    sindex = gpd.GeoSeries(geoms).sindex
    adj = {i: set() for i in range(n)}
    for i in range(n):
        if geoms[i] is None:
            continue
        possible = list(sindex.intersection(geoms[i].bounds))
        for j in possible:
            if j > i and geoms[j] is not None and geoms[i].intersects(geoms[j]):
                adj[i].add(j)
                adj[j].add(i)

    # Connected components
    visited = set()
    coalition_map = {}
    coalition_id = 0
    for i in range(n):
        if i in visited or geoms[i] is None:
            continue
        queue = [i]
        component = set()
        while queue:
            node = queue.pop(0)
            if node in visited:
                continue
            visited.add(node)
            component.add(node)
            for nb in adj[node]:
                if nb not in visited:
                    queue.append(nb)
        for node in component:
            coalition_map[node] = coalition_id
        coalition_id += 1

    station_gdf["coalition"] = [coalition_map.get(i, -1) for i in range(n)]

    log.info("  Found %d coalitions", station_gdf["coalition"].nunique())
    log.info("  Coalition distribution: %s",
             station_gdf["coalition"].value_counts().to_dict())

    return station_gdf


# =============================================================================
# 5.  Build output 1: land-clipped coalition zones
# =============================================================================


def build_coalition_output(station_gdf):
    """Build the station_spheres output."""
    log.info("Building output 1: station_spheres …")

    out = gpd.GeoDataFrame(
        {
            "coalition": station_gdf["coalition"].astype(int),
            "station_id": station_gdf["station_id"],
            "station_name": station_gdf["station_name"],
        },
        geometry=station_gdf["clipped_land"].values,
        crs=OUT_CRS,
    )

    log.info("  Output has %d rows", len(out))
    log.info("  Coalition values: %s", sorted(out["coalition"].unique()))

    return out


# =============================================================================
# 6.  Build output 2: over-water intersections
# =============================================================================


def build_water_output(station_gdf, water_all):
    """Compute over-water portion of each station's buffer and attribute with water."""
    log.info("Building output 2: station_water_overlap …")

    rows = []
    for _, row in station_gdf.iterrows():
        buf = row["buffer"]
        land_part = row["clipped_land"]

        # Over-water portion = full buffer minus land part
        ocean = buf.difference(land_part)
        if ocean.is_empty:
            continue

        # Normalise to MultiPolygon
        if ocean.geom_type == "GeometryCollection":
            polys = [g for g in ocean.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
            if not polys:
                continue
            ocean = unary_union(polys)
        if ocean.geom_type == "Polygon":
            ocean = MultiPolygon([ocean])
        elif ocean.geom_type != "MultiPolygon":
            continue

        # Find intersecting water features
        water_sub = water_all[water_all.intersects(ocean)]
        if len(water_sub) == 0:
            rows.append(
                {
                    "station_id": row["station_id"],
                    "station_name": row["station_name"],
                    "water_id": None,
                    "water_name": None,
                    "water_subtype": None,
                    "water_source": None,
                    "geometry": ocean,
                }
            )
        else:
            for _, wrow in water_sub.iterrows():
                inter = ocean.intersection(wrow.geometry)
                if inter.is_empty:
                    continue
                if inter.geom_type == "GeometryCollection":
                    polys = [g for g in inter.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
                    if not polys:
                        continue
                    inter = unary_union(polys)
                if inter.geom_type == "Polygon":
                    inter = MultiPolygon([inter])
                elif inter.geom_type != "MultiPolygon":
                    continue

                rows.append(
                    {
                        "station_id": row["station_id"],
                        "station_name": row["station_name"],
                        "water_id": wrow["water_id"],
                        "water_name": wrow["water_name"],
                        "water_subtype": wrow["water_subtype"],
                        "water_source": wrow["water_source"],
                        "geometry": inter,
                    }
                )

    out = gpd.GeoDataFrame(rows, crs=OUT_CRS)
    log.info("  Output has %d rows", len(out))

    return out


# =============================================================================
# 7.  Main
# =============================================================================


def main():
    stations = fetch_stations()
    if not stations:
        log.error("No stations found. Aborting.")
        sys.exit(1)

    land_union = fetch_land()
    water_all = fetch_water()

    station_gdf = build_buffers(stations, land_union)
    if len(station_gdf) == 0:
        log.error("No stations have non-empty land intersection. Aborting.")
        sys.exit(1)

    station_gdf = compute_coalitions(station_gdf)

    out1 = build_coalition_output(station_gdf)
    out1.to_parquet("/work/station_spheres.geoparquet")
    log.info("Wrote /work/station_spheres.geoparquet (%d rows)", len(out1))

    out2 = build_water_output(station_gdf, water_all)
    out2.to_parquet("/work/station_water_overlap.geoparquet")
    log.info("Wrote /work/station_water_overlap.geoparquet (%d rows)", len(out2))

    log.info("Done.")


if __name__ == "__main__":
    main()

Wrote /work/solve.py (19025 chars).

22:10

Let me test the station matching first to see if we get a good set of stations.

22:10

Bash cd /work && python3 -c "

cd /work && python3 -c "
from solve import fetch_stations, KNOWN_STATION_NAMES
stations = fetch_stations()
print(f'\nFound {len(stations)} stations')
for s in stations:
    print(f'  {s[\"station_name\"]}')
" 2>&1

2026-06-16 19:25:16,989 INFO Fetching Overture places south of -60° …
2026-06-16 19:25:38,382 INFO   2348 place features south of -60°
2026-06-16 19:25:38,468 INFO Fetching Overture division_area …
Traceback (most recent call last):
  File "<string>", line 3, in <module>
    stations = fetch_stations()
  File "/work/solve.py", line 257, in fetch_stations
    add_if_match(proxy)
    ~~~~~~~~~~~~^^^^^^^
  File "/work/solve.py", line 199, in add_if_match
    "station_id": row["id"],
                  ~~~^^^^^^
TypeError: 'RowProxy' object is not subscriptable

22:57

The RowProxy class doesn't support subscript access. Let me fix it.

22:57

Edit /work/solve.py

Edited /work/solve.py.

23:41

Now I need to also handle the fact that the `add_if_match` function uses `row.geometry` which may reference the wrong attribute for Point objects. Let me think about this more carefully.

For `places`, each row has a `row.geometry` which is a Shapely Point with `.x` and `.y`.
For `division_area`, the geometry is a Polygon, so we use `centroid`.

Let me restructure more carefully to handle both cases.

Also I need to make `add_if_match` not reference `row.geometry` directly but instead accept geometry as a parameter.

23:41

Write /work/solve.py

content · 496 lines · py

#!/usr/bin/env python3
"""
Cross-station logistics-overlap maps for Antarctic Treaty consultative meeting.

Produces two GeoParquet outputs in EPSG:3031 (Antarctic Polar Stereographic):
  1. station_spheres.geoparquet   – land-clipped station zones, grouped into
     coalitions wherever they overlap.  Columns: coalition (int), station_id,
     station_name, geometry (MultiPolygon).
  2. station_water_overlap.geoparquet – over-water portions of each station's
     200 km buffer, attributed with water features intersected.  Columns:
     station_id, station_name, water_id, water_name, water_subtype,
     water_source, geometry (MultiPolygon).
"""

import logging
import re
import sys

import geopandas as gpd
import numpy as np
import pandas as pd
from overturemaps.core import geodataframe
from shapely.geometry import MultiPolygon, Point, Polygon
from shapely.ops import unary_union

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
log = logging.getLogger(__name__)

# ── Antarctic bounding box ──────────────────────────────────
ANT_BBOX = (-180.0, -90.0, 180.0, -60.0)

# ── Output CRS (Antarctic Polar Stereographic) ───────────────
OUT_CRS = "EPSG:3031"

# ── Buffer radius in metres ──────────────────────────────────
BUFFER_M = 200_000

# =============================================================================
#  Known Antarctic research stations – exact primary-name matching
# =============================================================================

# Exact primary names (from Overture) that are actual research stations
KNOWN_STATION_NAMES = frozenset(
    s.casefold()
    for s in [
        "Base McMurdo",
        "Base antarctique McMurdo",
        "Polární stanice McMurdo",
        "Bellingshausen Russian Antarctic Station",
        "Carlini Base",
        "Esperanza Base, Antarctica",
        "Base Esperanza, Antartida Argentina",
        "Base Marambio, Antartida Argentina",
        "Marambio Base",
        "Base Antártica Marambio",
        "Neumayer-Station II",
        "Base antarctique Mawson",
        "Base antarctique Concordia",
        "Base antártica Concordia",
        "Base antarctique Dumont-d'Urville",
        "Brown Station",
        "Brown Station, Antarctic Peninsula",
        "Palmer Station, Antarctica",
        "Antarctic Kunlun Station",
        "Vostok İstasyonu",
        "Plateau Station",
        "Mario-Zucchelli-Station",
        "González Videla Antarctic Base",
        "Troll Airfield",
        "Antarctica/Troll",
        "SANAE",
        "Rothera Research Station",
        "McMurdo General Hospital",
        "Lake Vostok",
    ]
)

# Additional safe search terms (using word boundaries to avoid false positives)
SAFE_TERMS = [
    "mcmurdo", "amundsen-scott", "scott base", "mawson",
    "davis station", "casey station", "halley", "rothera",
    "san martin", "progress", "novolazarevskaya", "vostok",
    "concordia", "kunlun", "troll", "neumayer", "kohnen",
    "princess elisabeth", "syowa", "dumont", "mirny",
    "bellingshausen", "great wall", "zhongshan", "taishan",
    "jang bogo", "king sejong", "mario zucchelli", "zucchelli",
    "vernadsky", "carlini", "esperanza", "marambio",
    "orcadas", "brown station", "palmer station", "sanae",
    "maitri", "bharati", "ferraz", "ohiggins", "frei",
    "escudero", "artigas", "prat", "gabriel de castilla",
    "juan carlos i", "melchior", "belgrano", "matienzo",
    "decepcion", "petrel", "camara", "primavera", "teniente",
    "gonzalez videla", "gonzález videla", "plateau station",
    "byrd station", "siple station", "molodezhnaya",
    "leningradskaya", "mizuho", "asuka",
    # Additional term to catch Tor station without false positives
    "tor station",
]

SEARCH_PATTERNS = []
for term in SAFE_TERMS:
    if len(term) <= 5:
        # Use word boundaries for short terms
        SEARCH_PATTERNS.append(re.compile(r"\b" + re.escape(term) + r"\b", re.IGNORECASE))
    else:
        SEARCH_PATTERNS.append(re.compile(re.escape(term), re.IGNORECASE))


def _name_to_point(name, geometry):
    """Convert a row geometry to a Point, handling Polygon/Point."""
    if hasattr(geometry, 'x') and hasattr(geometry, 'y'):
        return Point(geometry.x, geometry.y)
    else:
        return geometry.centroid


def _check_and_add(stations_list, matched_ids, row_id, names_dict, geometry, search_pats):
    """Check if a feature matches a station name and add to list if so."""
    if not isinstance(names_dict, dict):
        return False
    name = names_dict.get("primary", "")
    if not name:
        return False
    name_lower = name.casefold()

    # Check exact match
    if name_lower in KNOWN_STATION_NAMES:
        pt = _name_to_point(name, geometry)
        stations_list.append({
            "station_id": row_id,
            "station_name": name,
            "geometry": pt,
            "lat": pt.y,
            "lon": pt.x,
        })
        matched_ids.add(row_id)
        return True

    # Check pattern match
    for pat in search_pats:
        if pat.search(name):
            pt = _name_to_point(name, geometry)
            stations_list.append({
                "station_id": row_id,
                "station_name": name,
                "geometry": pt,
                "lat": pt.y,
                "lon": pt.x,
            })
            matched_ids.add(row_id)
            return True

    return False


def fetch_stations():
    """Fetch Antarctic research station locations from Overture data."""
    log.info("Fetching Overture places south of -60° …")
    places = geodataframe("place", bbox=ANT_BBOX, stac=True)

    mask = places.geometry.y < -60.0
    places = places[mask].copy()
    log.info("  %d place features south of -60°", len(places))

    stations = []
    matched_ids = set()

    for _, row in places.iterrows():
        _check_and_add(stations, matched_ids, row["id"],
                       row.get("names"), row.geometry, SEARCH_PATTERNS)

    # Also check division_area for additional stations (Rothera etc.)
    log.info("Fetching Overture division_area …")
    div = geodataframe("division_area", bbox=ANT_BBOX, stac=True)
    for _, row in div.iterrows():
        if row["id"] in matched_ids:
            continue
        names = row.get("names")
        if not isinstance(names, dict):
            continue
        name = names.get("primary", "")
        if not name:
            continue
        geom = row.geometry
        if geom.centroid.y >= -60.0:
            continue

        _check_and_add(stations, matched_ids, row["id"],
                       names, geom, SEARCH_PATTERNS)

    # Deduplicate by station_name
    seen = set()
    unique = []
    for s in stations:
        key = s["station_name"].casefold().strip()
        if key not in seen:
            seen.add(key)
            unique.append(s)

    log.info("  Found %d unique stations", len(unique))
    for s in sorted(unique, key=lambda x: x["station_name"]):
        log.info("    %s  (%.4f, %.4f)", s["station_name"], s["lon"], s["lat"])

    return unique


# =============================================================================
# 2.  Fetch Antarctic landmass and water features
# =============================================================================


def fetch_land():
    """Fetch Antarctic land from Overture 'land' theme."""
    log.info("Fetching Overture land data …")
    land = geodataframe("land", bbox=ANT_BBOX, stac=True)

    mask = land.geometry.centroid.y < -60.0
    land = land[mask].copy()
    log.info("  %d land features south of -60°", len(land))

    land = land.to_crs(OUT_CRS)

    land_union = unary_union(land.geometry.values)
    log.info("  Land union done")

    return land_union


def fetch_water():
    """Fetch Antarctic water features from Overture 'water' and 'bathymetry'."""
    log.info("Fetching Overture water data …")
    water = geodataframe("water", bbox=ANT_BBOX, stac=True)
    mask = water.geometry.centroid.y < -60.0
    water = water[mask].copy()
    log.info("  %d water features south of -60°", len(water))

    water = water.to_crs(OUT_CRS)
    water["water_source"] = "base.water"

    def get_name(r):
        n = r.get("names")
        return n.get("primary", "") if isinstance(n, dict) else ""

    water["water_name"] = water.apply(get_name, axis=1)
    water = water.rename(columns={"id": "water_id", "subtype": "water_subtype"})
    water = water[["water_id", "water_name", "water_subtype", "water_source", "geometry"]]

    log.info("Fetching Overture bathymetry data …")
    bathy = geodataframe("bathymetry", bbox=ANT_BBOX, stac=True)
    mask = bathy.geometry.centroid.y < -60.0
    bathy = bathy[mask].copy()
    log.info("  %d bathymetry features south of -60°", len(bathy))

    bathy = bathy.to_crs(OUT_CRS)
    bathy["water_source"] = "base.bathymetry"
    bathy["water_id"] = bathy["id"]
    bathy["water_subtype"] = "bathymetry"
    bathy["water_name"] = bathy.apply(get_name, axis=1)
    bathy = bathy[["water_id", "water_name", "water_subtype", "water_source", "geometry"]]

    water_all = pd.concat([water, bathy], ignore_index=True)
    log.info("  Total water+bathymetry features: %d", len(water_all))

    return water_all


# =============================================================================
# 3.  Build 200 km buffer zones, clip to land
# =============================================================================


def build_buffers(stations, land_union):
    """Build 200 km buffers, clip to land."""
    log.info("Building 200 km buffers and clipping to land …")

    station_gdf = gpd.GeoDataFrame(
        stations, geometry="geometry", crs="EPSG:4326"
    ).to_crs(OUT_CRS)

    station_gdf["buffer"] = station_gdf.geometry.buffer(BUFFER_M, resolution=64)

    def clip_to_land(buf):
        inter = buf.intersection(land_union)
        if inter.is_empty:
            return None
        if inter.geom_type == "Polygon":
            return MultiPolygon([inter])
        elif inter.geom_type == "MultiPolygon":
            return inter
        elif inter.geom_type == "GeometryCollection":
            polys = [g for g in inter.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
            if not polys:
                return None
            merged = unary_union(polys)
            if merged.geom_type == "Polygon":
                return MultiPolygon([merged])
            elif merged.geom_type == "MultiPolygon":
                return merged
        return None

    station_gdf["clipped_land"] = station_gdf["buffer"].apply(clip_to_land)
    station_gdf = station_gdf[station_gdf["clipped_land"].notna()].copy()
    log.info("  %d stations have non-empty land intersection", len(station_gdf))

    return station_gdf


# =============================================================================
# 4.  Compute coalition groups (overlapping buffers on land)
# =============================================================================


def compute_coalitions(station_gdf):
    """Group overlapping station land polygons into coalitions."""
    log.info("Computing coalition groups …")

    geoms = station_gdf["clipped_land"].values
    n = len(geoms)
    if n == 0:
        station_gdf["coalition"] = []
        return station_gdf

    # Build adjacency via spatial index
    sindex = gpd.GeoSeries(geoms).sindex
    adj = {i: set() for i in range(n)}
    for i in range(n):
        if geoms[i] is None:
            continue
        possible = list(sindex.intersection(geoms[i].bounds))
        for j in possible:
            if j > i and geoms[j] is not None and geoms[i].intersects(geoms[j]):
                adj[i].add(j)
                adj[j].add(i)

    # Connected components
    visited = set()
    coalition_map = {}
    coalition_id = 0
    for i in range(n):
        if i in visited or geoms[i] is None:
            continue
        queue = [i]
        component = set()
        while queue:
            node = queue.pop(0)
            if node in visited:
                continue
            visited.add(node)
            component.add(node)
            for nb in adj[node]:
                if nb not in visited:
                    queue.append(nb)
        for node in component:
            coalition_map[node] = coalition_id
        coalition_id += 1

    station_gdf["coalition"] = [coalition_map.get(i, -1) for i in range(n)]

    log.info("  Found %d coalitions", station_gdf["coalition"].nunique())
    log.info("  Coalition distribution: %s",
             station_gdf["coalition"].value_counts().to_dict())

    return station_gdf


# =============================================================================
# 5.  Build output 1: land-clipped coalition zones
# =============================================================================


def build_coalition_output(station_gdf):
    """Build the station_spheres output."""
    log.info("Building output 1: station_spheres …")

    out = gpd.GeoDataFrame(
        {
            "coalition": station_gdf["coalition"].astype(int),
            "station_id": station_gdf["station_id"],
            "station_name": station_gdf["station_name"],
        },
        geometry=station_gdf["clipped_land"].values,
        crs=OUT_CRS,
    )

    log.info("  Output has %d rows", len(out))
    log.info("  Coalition values: %s", sorted(out["coalition"].unique()))

    return out


# =============================================================================
# 6.  Build output 2: over-water intersections
# =============================================================================


def build_water_output(station_gdf, water_all):
    """Compute over-water portion of each station's buffer and attribute with water."""
    log.info("Building output 2: station_water_overlap …")

    rows = []
    for _, row in station_gdf.iterrows():
        buf = row["buffer"]
        land_part = row["clipped_land"]

        ocean = buf.difference(land_part)
        if ocean.is_empty:
            continue

        # Normalise to MultiPolygon
        if ocean.geom_type == "GeometryCollection":
            polys = [g for g in ocean.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
            if not polys:
                continue
            ocean = unary_union(polys)
        if ocean.geom_type == "Polygon":
            ocean = MultiPolygon([ocean])
        elif ocean.geom_type != "MultiPolygon":
            continue

        # Find intersecting water features
        water_sub = water_all[water_all.intersects(ocean)]
        if len(water_sub) == 0:
            rows.append({
                "station_id": row["station_id"],
                "station_name": row["station_name"],
                "water_id": None,
                "water_name": None,
                "water_subtype": None,
                "water_source": None,
                "geometry": ocean,
            })
        else:
            for _, wrow in water_sub.iterrows():
                inter = ocean.intersection(wrow.geometry)
                if inter.is_empty:
                    continue
                if inter.geom_type == "GeometryCollection":
                    polys = [g for g in inter.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
                    if not polys:
                        continue
                    inter = unary_union(polys)
                if inter.geom_type == "Polygon":
                    inter = MultiPolygon([inter])
                elif inter.geom_type != "MultiPolygon":
                    continue

                rows.append({
                    "station_id": row["station_id"],
                    "station_name": row["station_name"],
                    "water_id": wrow["water_id"],
                    "water_name": wrow["water_name"],
                    "water_subtype": wrow["water_subtype"],
                    "water_source": wrow["water_source"],
                    "geometry": inter,
                })

    out = gpd.GeoDataFrame(rows, crs=OUT_CRS)
    log.info("  Output has %d rows", len(out))

    return out


# =============================================================================
# 7.  Main
# =============================================================================


def main():
    stations = fetch_stations()
    if not stations:
        log.error("No stations found. Aborting.")
        sys.exit(1)

    land_union = fetch_land()
    water_all = fetch_water()

    station_gdf = build_buffers(stations, land_union)
    if len(station_gdf) == 0:
        log.error("No stations have non-empty land intersection. Aborting.")
        sys.exit(1)

    station_gdf = compute_coalitions(station_gdf)

    out1 = build_coalition_output(station_gdf)
    out1.to_parquet("/work/station_spheres.geoparquet")
    log.info("Wrote /work/station_spheres.geoparquet (%d rows)", len(out1))

    out2 = build_water_output(station_gdf, water_all)
    out2.to_parquet("/work/station_water_overlap.geoparquet")
    log.info("Wrote /work/station_water_overlap.geoparquet (%d rows)", len(out2))

    log.info("Done.")


if __name__ == "__main__":
    main()

Wrote /work/solve.py (17108 chars).

24:07

Let me test the station finding first to see what we get.

24:07

Bash cd /work && timeout 300 python3 -c "

cd /work && timeout 300 python3 -c "
from solve import fetch_stations
stations = fetch_stations()
print(f'\nFound {len(stations)} stations:')
for s in sorted(stations, key=lambda x: x['station_name']):
    print(f'  {s[\"station_name\"]:55s} ({s[\"lon\"]:9.4f}, {s[\"lat\"]:8.4f})')
" 2>&1

2026-06-16 19:27:15,045 INFO Fetching Overture places south of -60° …
2026-06-16 19:27:31,157 INFO   2348 place features south of -60°
2026-06-16 19:27:31,257 INFO Fetching Overture division_area …
2026-06-16 19:27:46,871 INFO   Found 29 unique stations
2026-06-16 19:27:46,871 INFO     Antarctic Kunlun Station  (77.1161, -80.4169)
2026-06-16 19:27:46,871 INFO     Antarctica/Troll  (2.5350, -72.0114)
2026-06-16 19:27:46,871 INFO     Base Antártica Marambio  (-56.6235, -64.2374)
2026-06-16 19:27:46,871 INFO     Base Esperanza, Antartida Argentina  (-56.9974, -63.3980)
2026-06-16 19:27:46,871 INFO     Base Marambio, Antartida Argentina  (-56.6198, -64.2413)
2026-06-16 19:27:46,871 INFO     Base McMurdo  (166.6667, -77.8500)
2026-06-16 19:27:46,871 INFO     Base antarctique Concordia  (123.5580, -75.1000)
2026-06-16 19:27:46,871 INFO     Base antarctique Dumont-d'Urville  (140.0013, -66.6628)
2026-06-16 19:27:46,871 INFO     Base antarctique Mawson  (62.8736, -67.6026)
2026-06-16 19:27:46,871 INFO     Base antarctique McMurdo  (166.6667, -77.8500)
2026-06-16 19:27:46,871 INFO     Base antártica Concordia  (123.3333, -75.1000)
2026-06-16 19:27:46,871 INFO     Bellingshausen Russian Antarctic Station  (-58.9624, -62.1998)
2026-06-16 19:27:46,871 INFO     Brown Station  (-62.8700, -64.8950)
2026-06-16 19:27:46,871 INFO     Brown Station, Antarctic Peninsula  (-62.8724, -64.8921)
2026-06-16 19:27:46,871 INFO     Carlini Base  (-58.6668, -62.2379)
2026-06-16 19:27:46,871 INFO     Esperanza Base, Antarctica  (-56.9961, -63.3977)
2026-06-16 19:27:46,871 INFO     González Videla Antarctic Base  (-62.8575, -64.8234)
2026-06-16 19:27:46,871 INFO     Lake Vostok  (106.0000, -77.5000)
2026-06-16 19:27:46,872 INFO     Marambio Base  (-56.6268, -64.2410)
2026-06-16 19:27:46,872 INFO     Mario-Zucchelli-Station  (164.1125, -74.6943)
2026-06-16 19:27:46,872 INFO     McMurdo General Hospital  (166.6693, -77.8468)
2026-06-16 19:27:46,872 INFO     Neumayer-Station II  (-8.2500, -70.6500)
2026-06-16 19:27:46,872 INFO     Palmer Station, Antarctica  (-64.0531, -64.7743)
2026-06-16 19:27:46,872 INFO     Plateau Station  (40.5000, -79.2500)
2026-06-16 19:27:46,872 INFO     Polární stanice McMurdo  (166.6667, -77.8500)
2026-06-16 19:27:46,872 INFO     Rothera Research Station  (-68.1235, -67.5690)
2026-06-16 19:27:46,872 INFO     SANAE  (2.8500, -72.6667)
2026-06-16 19:27:46,872 INFO     Troll Airfield  (2.4667, -71.9572)
2026-06-16 19:27:46,872 INFO     Vostok İstasyonu  (106.8373, -78.4644)

Found 29 stations:
  Antarctic Kunlun Station                                (  77.1161, -80.4169)
  Antarctica/Troll                                        (   2.5350, -72.0114)
  Base Antártica Marambio                                 ( -56.6235, -64.2374)
  Base Esperanza, Antartida Argentina                     ( -56.9974, -63.3980)
  Base Marambio, Antartida Argentina                      ( -56.6198, -64.2413)
  Base McMurdo                                            ( 166.6667, -77.8500)
  Base antarctique Concordia                              ( 123.5580, -75.1000)
  Base antarctique Dumont-d'Urville                       ( 140.0013, -66.6628)
  Base antarctique Mawson                                 (  62.8736, -67.6026)
  Base antarctique McMurdo                                ( 166.6667, -77.8500)
  Base antártica Concordia                                ( 123.3333, -75.1000)
  Bellingshausen Russian Antarctic Station                ( -58.9624, -62.1998)
  Brown Station                                           ( -62.8700, -64.8950)
  Brown Station, Antarctic Peninsula                      ( -62.8724, -64.8921)
  Carlini Base                                            ( -58.6668, -62.2379)
  Esperanza Base, Antarctica                              ( -56.9961, -63.3977)
  González Videla Antarctic Base                          ( -62.8575, -64.8234)
  Lake Vostok                                             ( 106.0000, -77.5000)
  Marambio Base                                           ( -56.6268, -64.2410)
  Mario-Zucchelli-Station                                 ( 164.1125, -74.6943)
  McMurdo General Hospital                                ( 166.6693, -77.8468)
  Neumayer-Station II                                     (  -8.2500, -70.6500)
  Palmer Station, Antarctica                              ( -64.0531, -64.7743)
  Plateau Station                                         (  40.5000, -79.2500)
  Polární stanice McMurdo                                 ( 166.6667, -77.8500)
  Rothera Research Station                                ( -68.1235, -67.5690)
  SANAE                                                   (   2.8500, -72.6667)
  Troll Airfield                                          (   2.4667, -71.9572)
  Vostok İstasyonu                                        ( 106.8373, -78.4644)

25:17

Good - 29 unique stations found. Now there are some duplicates that should be merged: Base McMurdo, Base antarctique McMurdo, Polární stanice McMurdo are all the same station. Same for Base Marambio/Marambio Base/Base Antártica Marambio and Base Esperanza/Esperanza Base, etc.

Let me think about how to handle the deduplication. The station_id will differ for different Overture records of the same station. The spec says each feature needs `station_id` and `station_name`. 

I think the best approach is to map multiple Overture records for the same actual station to a single canonical station. Let me add a deduplication mapping.

Let me also check: Base McMurdo (166.6667, -77.8500), Base antarctique McMurdo (166.6667, -77.8500), Polární stanice McMurdo (166.6667, -77.8500) - these are at the same coordinates. Let me merge them by grouping by location.

25:17

Write /work/solve.py

content · 522 lines · py

#!/usr/bin/env python3
"""
Cross-station logistics-overlap maps for Antarctic Treaty consultative meeting.

Produces two GeoParquet outputs in EPSG:3031 (Antarctic Polar Stereographic):
  1. station_spheres.geoparquet   – land-clipped station zones, grouped into
     coalitions wherever they overlap.  Columns: coalition (int), station_id,
     station_name, geometry (MultiPolygon).
  2. station_water_overlap.geoparquet – over-water portions of each station's
     200 km buffer, attributed with water features intersected.  Columns:
     station_id, station_name, water_id, water_name, water_subtype,
     water_source, geometry (MultiPolygon).
"""

import logging
import re
import sys

import geopandas as gpd
import numpy as np
import pandas as pd
from overturemaps.core import geodataframe
from shapely.geometry import MultiPolygon, Point, Polygon
from shapely.ops import unary_union
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import haversine_distances
import math

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
log = logging.getLogger(__name__)

# ── Antarctic bounding box ──────────────────────────────────
ANT_BBOX = (-180.0, -90.0, 180.0, -60.0)

# ── Output CRS (Antarctic Polar Stereographic) ───────────────
OUT_CRS = "EPSG:3031"

# ── Buffer radius in metres ──────────────────────────────────
BUFFER_M = 200_000

# ── Distance threshold (km) for grouping duplicate stations ──
DUPLICATE_KM = 5.0   # 5 km — any stations closer than this are merged

# =============================================================================
#  Known Antarctic research stations – exact primary-name matching
# =============================================================================

# Exact primary names (from Overture) that are actual research stations
KNOWN_STATION_NAMES = frozenset(
    s.casefold()
    for s in [
        "Base McMurdo",
        "Base antarctique McMurdo",
        "Polární stanice McMurdo",
        "Bellingshausen Russian Antarctic Station",
        "Carlini Base",
        "Esperanza Base, Antarctica",
        "Base Esperanza, Antartida Argentina",
        "Base Marambio, Antartida Argentina",
        "Marambio Base",
        "Base Antártica Marambio",
        "Neumayer-Station II",
        "Base antarctique Mawson",
        "Base antarctique Concordia",
        "Base antártica Concordia",
        "Base antarctique Dumont-d'Urville",
        "Brown Station",
        "Brown Station, Antarctic Peninsula",
        "Palmer Station, Antarctica",
        "Antarctic Kunlun Station",
        "Vostok İstasyonu",
        "Plateau Station",
        "Mario-Zucchelli-Station",
        "González Videla Antarctic Base",
        "Troll Airfield",
        "Antarctica/Troll",
        "SANAE",
        "Rothera Research Station",
        "McMurdo General Hospital",
        "Lake Vostok",
    ]
)

# Additional safe search terms (word-boundary for short terms)
SAFE_TERMS = [
    "mcmurdo", "amundsen-scott", "scott base", "mawson",
    "davis station", "casey station", "halley", "rothera",
    "san martin", "progress", "novolazarevskaya", "vostok",
    "concordia", "kunlun", "troll", "neumayer", "kohnen",
    "princess elisabeth", "syowa", "dumont", "mirny",
    "bellingshausen", "great wall", "zhongshan", "taishan",
    "jang bogo", "king sejong", "mario zucchelli", "zucchelli",
    "vernadsky", "carlini", "esperanza", "marambio",
    "orcadas", "brown station", "palmer station", "sanae",
    "maitri", "bharati", "ferraz", "ohiggins", "frei",
    "escudero", "artigas", "prat", "gabriel de castilla",
    "juan carlos i", "melchior", "belgrano", "matienzo",
    "decepcion", "petrel", "camara", "primavera", "teniente",
    "gonzalez videla", "gonzález videla", "plateau station",
    "byrd station", "siple station", "molodezhnaya",
    "leningradskaya", "mizuho", "asuka",
    "tor station",
]

SEARCH_PATTERNS = []
for term in SAFE_TERMS:
    if len(term) <= 5:
        SEARCH_PATTERNS.append(re.compile(r"\b" + re.escape(term) + r"\b", re.IGNORECASE))
    else:
        SEARCH_PATTERNS.append(re.compile(re.escape(term), re.IGNORECASE))


def _check_and_add(stations_list, matched_ids, row_id, names_dict, geometry, search_pats):
    """Check if a feature matches a station name and add to list if so."""
    if not isinstance(names_dict, dict):
        return False
    name = names_dict.get("primary", "")
    if not name:
        return False
    name_lower = name.casefold()

    # Determine point geometry
    if hasattr(geometry, 'x') and hasattr(geometry, 'y'):
        pt = Point(geometry.x, geometry.y)
    else:
        pt = geometry.centroid

    # Check exact match
    if name_lower in KNOWN_STATION_NAMES:
        stations_list.append({
            "station_id": row_id,
            "station_name": name,
            "geometry": pt,
        })
        matched_ids.add(row_id)
        return True

    # Check pattern match
    for pat in search_pats:
        if pat.search(name):
            stations_list.append({
                "station_id": row_id,
                "station_name": name,
                "geometry": pt,
            })
            matched_ids.add(row_id)
            return True

    return False


def _merge_duplicates(stations):
    """Merge stations that are within DUPLICATE_KM of each other."""
    if len(stations) <= 1:
        return stations

    # Prepare coords in radians for haversine
    coords_rad = np.radians([[s["geometry"].y, s["geometry"].x] for s in stations])

    # DBSCAN with epsilon = DUPLICATE_KM in radians
    eps_rad = DUPLICATE_KM / 6371.0  # Earth radius ~6371 km
    clustering = DBSCAN(eps=eps_rad, min_samples=1, metric="haversine").fit(coords_rad)

    merged = {}
    for label, s in zip(clustering.labels_, stations):
        if label not in merged:
            merged[label] = {
                "station_id": s["station_id"],
                "station_name": s["station_name"],
                "geometry": s["geometry"],
            }
        else:
            # Keep the English/simpler name if available
            existing = merged[label]
            existing_names = [existing["station_name"], s["station_name"]]
            # Prefer shorter names (likely the canonical one)
            merged[label]["station_name"] = min(existing_names, key=lambda x: len(x))

    result = list(merged.values())
    log.info("  Merged %d raw station records into %d unique stations",
             len(stations), len(result))
    return result


def fetch_stations():
    """Fetch Antarctic research station locations from Overture data."""
    log.info("Fetching Overture places south of -60° …")
    places = geodataframe("place", bbox=ANT_BBOX, stac=True)

    mask = places.geometry.y < -60.0
    places = places[mask].copy()
    log.info("  %d place features south of -60°", len(places))

    stations = []
    matched_ids = set()

    for _, row in places.iterrows():
        _check_and_add(stations, matched_ids, row["id"],
                       row.get("names"), row.geometry, SEARCH_PATTERNS)

    # Also check division_area for additional stations
    log.info("Fetching Overture division_area …")
    div = geodataframe("division_area", bbox=ANT_BBOX, stac=True)
    for _, row in div.iterrows():
        if row["id"] in matched_ids:
            continue
        names = row.get("names")
        if not isinstance(names, dict):
            continue
        name = names.get("primary", "")
        if not name:
            continue
        if row.geometry.centroid.y >= -60.0:
            continue
        _check_and_add(stations, matched_ids, row["id"],
                       names, row.geometry, SEARCH_PATTERNS)

    # Merge duplicates by location
    stations = _merge_duplicates(stations)

    # Add lat/lon for logging
    for s in stations:
        s["lat"] = s["geometry"].y
        s["lon"] = s["geometry"].x

    log.info("  Final unique stations: %d", len(stations))
    for s in sorted(stations, key=lambda x: x["station_name"]):
        log.info("    %s  (%.4f, %.4f)", s["station_name"], s["lon"], s["lat"])

    return stations


# =============================================================================
# 2.  Fetch Antarctic landmass and water features
# =============================================================================


def fetch_land():
    """Fetch Antarctic land from Overture 'land' theme."""
    log.info("Fetching Overture land data …")
    land = geodataframe("land", bbox=ANT_BBOX, stac=True)

    mask = land.geometry.centroid.y < -60.0
    land = land[mask].copy()
    log.info("  %d land features south of -60°", len(land))

    land = land.to_crs(OUT_CRS)

    land_union = unary_union(land.geometry.values)
    log.info("  Land union done")

    return land_union


def fetch_water():
    """Fetch Antarctic water features from Overture 'water' and 'bathymetry'."""
    log.info("Fetching Overture water data …")
    water = geodataframe("water", bbox=ANT_BBOX, stac=True)
    mask = water.geometry.centroid.y < -60.0
    water = water[mask].copy()
    log.info("  %d water features south of -60°", len(water))

    water = water.to_crs(OUT_CRS)
    water["water_source"] = "base.water"

    def get_name(r):
        n = r.get("names")
        return n.get("primary", "") if isinstance(n, dict) else ""

    water["water_name"] = water.apply(get_name, axis=1)
    water = water.rename(columns={"id": "water_id", "subtype": "water_subtype"})
    water = water[["water_id", "water_name", "water_subtype", "water_source", "geometry"]]

    log.info("Fetching Overture bathymetry data …")
    bathy = geodataframe("bathymetry", bbox=ANT_BBOX, stac=True)
    mask = bathy.geometry.centroid.y < -60.0
    bathy = bathy[mask].copy()
    log.info("  %d bathymetry features south of -60°", len(bathy))

    bathy = bathy.to_crs(OUT_CRS)
    bathy["water_source"] = "base.bathymetry"
    bathy["water_id"] = bathy["id"]
    bathy["water_subtype"] = "bathymetry"
    bathy["water_name"] = bathy.apply(get_name, axis=1)
    bathy = bathy[["water_id", "water_name", "water_subtype", "water_source", "geometry"]]

    water_all = pd.concat([water, bathy], ignore_index=True)
    log.info("  Total water+bathymetry features: %d", len(water_all))

    return water_all


# =============================================================================
# 3.  Build 200 km buffer zones, clip to land
# =============================================================================


def build_buffers(stations, land_union):
    """Build 200 km buffers, clip to land."""
    log.info("Building 200 km buffers and clipping to land …")

    station_gdf = gpd.GeoDataFrame(
        stations, geometry="geometry", crs="EPSG:4326"
    ).to_crs(OUT_CRS)

    station_gdf["buffer"] = station_gdf.geometry.buffer(BUFFER_M, resolution=64)

    def clip_to_land(buf):
        inter = buf.intersection(land_union)
        if inter.is_empty:
            return None
        if inter.geom_type == "Polygon":
            return MultiPolygon([inter])
        elif inter.geom_type == "MultiPolygon":
            return inter
        elif inter.geom_type == "GeometryCollection":
            polys = [g for g in inter.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
            if not polys:
                return None
            merged = unary_union(polys)
            if merged.geom_type == "Polygon":
                return MultiPolygon([merged])
            elif merged.geom_type == "MultiPolygon":
                return merged
        return None

    station_gdf["clipped_land"] = station_gdf["buffer"].apply(clip_to_land)
    station_gdf = station_gdf[station_gdf["clipped_land"].notna()].copy()
    log.info("  %d stations have non-empty land intersection", len(station_gdf))

    return station_gdf


# =============================================================================
# 4.  Compute coalition groups (overlapping buffers on land)
# =============================================================================


def compute_coalitions(station_gdf):
    """Group overlapping station land polygons into coalitions."""
    log.info("Computing coalition groups …")

    geoms = station_gdf["clipped_land"].values
    n = len(geoms)
    if n == 0:
        station_gdf["coalition"] = []
        return station_gdf

    # Build adjacency via spatial index
    sindex = gpd.GeoSeries(geoms).sindex
    adj = {i: set() for i in range(n)}
    for i in range(n):
        if geoms[i] is None:
            continue
        possible = list(sindex.intersection(geoms[i].bounds))
        for j in possible:
            if j > i and geoms[j] is not None and geoms[i].intersects(geoms[j]):
                adj[i].add(j)
                adj[j].add(i)

    # Connected components
    visited = set()
    coalition_map = {}
    coalition_id = 0
    for i in range(n):
        if i in visited or geoms[i] is None:
            continue
        queue = [i]
        component = set()
        while queue:
            node = queue.pop(0)
            if node in visited:
                continue
            visited.add(node)
            component.add(node)
            for nb in adj[node]:
                if nb not in visited:
                    queue.append(nb)
        for node in component:
            coalition_map[node] = coalition_id
        coalition_id += 1

    station_gdf["coalition"] = [coalition_map.get(i, -1) for i in range(n)]

    log.info("  Found %d coalitions", station_gdf["coalition"].nunique())
    log.info("  Coalition distribution: %s",
             station_gdf["coalition"].value_counts().to_dict())

    return station_gdf


# =============================================================================
# 5.  Build output 1: land-clipped coalition zones
# =============================================================================


def build_coalition_output(station_gdf):
    """Build the station_spheres output."""
    log.info("Building output 1: station_spheres …")

    out = gpd.GeoDataFrame(
        {
            "coalition": station_gdf["coalition"].astype(int),
            "station_id": station_gdf["station_id"],
            "station_name": station_gdf["station_name"],
        },
        geometry=station_gdf["clipped_land"].values,
        crs=OUT_CRS,
    )

    log.info("  Output has %d rows", len(out))
    log.info("  Coalition values: %s", sorted(out["coalition"].unique()))

    return out


# =============================================================================
# 6.  Build output 2: over-water intersections
# =============================================================================


def build_water_output(station_gdf, water_all):
    """Compute over-water portion of each station's buffer and attribute with water."""
    log.info("Building output 2: station_water_overlap …")

    rows = []
    for _, row in station_gdf.iterrows():
        buf = row["buffer"]
        land_part = row["clipped_land"]

        ocean = buf.difference(land_part)
        if ocean.is_empty:
            continue

        # Normalise to MultiPolygon
        if ocean.geom_type == "GeometryCollection":
            polys = [g for g in ocean.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
            if not polys:
                continue
            ocean = unary_union(polys)
        if ocean.geom_type == "Polygon":
            ocean = MultiPolygon([ocean])
        elif ocean.geom_type != "MultiPolygon":
            continue

        # Find intersecting water features
        water_sub = water_all[water_all.intersects(ocean)]
        if len(water_sub) == 0:
            rows.append({
                "station_id": row["station_id"],
                "station_name": row["station_name"],
                "water_id": None,
                "water_name": None,
                "water_subtype": None,
                "water_source": None,
                "geometry": ocean,
            })
        else:
            for _, wrow in water_sub.iterrows():
                inter = ocean.intersection(wrow.geometry)
                if inter.is_empty:
                    continue
                if inter.geom_type == "GeometryCollection":
                    polys = [g for g in inter.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
                    if not polys:
                        continue
                    inter = unary_union(polys)
                if inter.geom_type == "Polygon":
                    inter = MultiPolygon([inter])
                elif inter.geom_type != "MultiPolygon":
                    continue

                rows.append({
                    "station_id": row["station_id"],
                    "station_name": row["station_name"],
                    "water_id": wrow["water_id"],
                    "water_name": wrow["water_name"],
                    "water_subtype": wrow["water_subtype"],
                    "water_source": wrow["water_source"],
                    "geometry": inter,
                })

    out = gpd.GeoDataFrame(rows, crs=OUT_CRS)
    log.info("  Output has %d rows", len(out))

    return out


# =============================================================================
# 7.  Main
# =============================================================================


def main():
    stations = fetch_stations()
    if not stations:
        log.error("No stations found. Aborting.")
        sys.exit(1)

    land_union = fetch_land()
    water_all = fetch_water()

    station_gdf = build_buffers(stations, land_union)
    if len(station_gdf) == 0:
        log.error("No stations have non-empty land intersection. Aborting.")
        sys.exit(1)

    station_gdf = compute_coalitions(station_gdf)

    out1 = build_coalition_output(station_gdf)
    out1.to_parquet("/work/station_spheres.geoparquet")
    log.info("Wrote /work/station_spheres.geoparquet (%d rows)", len(out1))

    out2 = build_water_output(station_gdf, water_all)
    out2.to_parquet("/work/station_water_overlap.geoparquet")
    log.info("Wrote /work/station_water_overlap.geoparquet (%d rows)", len(out2))

    log.info("Done.")


if __name__ == "__main__":
    main()

Wrote /work/solve.py (18171 chars).

25:32

Wait, I imported `sklearn` but it might not be installed. Let me use a simpler approach for merging duplicates - just group by rounded coordinates. Also, I need to double-check that `sklearn` is installed.

25:32

Bash python3 -c "import sklearn; print(sklearn.__version__)" 2>&1

Traceback (most recent call last):
  File "<string>", line 1, in <module>
    import sklearn; print(sklearn.__version__)
    ^^^^^^^^^^^^^^
ModuleNotFoundError: No module named 'sklearn'

25:49

I need to install sklearn or avoid using it for DBSCAN. Let me use a simple distance-based clustering instead.

25:49

Edit /work/solve.py

Edited /work/solve.py.

26:12

Now I need to replace the sklearn-based duplicate merge with a simple spatial join approach using GeoPandas:

26:12

Edit /work/solve.py

Edited /work/solve.py.

27:55

Now let me run the full script and see how it goes.

27:55

Bash cd /work && timeout 600 python solve.py 2>&1

2026-06-16 19:31:02,566 INFO Fetching Overture places south of -60° …
2026-06-16 19:31:17,048 INFO   2348 place features south of -60°
2026-06-16 19:31:17,204 INFO Fetching Overture division_area …
2026-06-16 19:31:32,975 INFO   Merged 29 raw station records into 22 unique stations
2026-06-16 19:31:32,975 INFO   Final unique stations: 22
2026-06-16 19:31:32,975 INFO     Antarctic Kunlun Station  (1017285.6722, 232688.4836)
2026-06-16 19:31:32,975 INFO     Antarctica/Troll  (87135.6338, 1968144.3633)
2026-06-16 19:31:32,975 INFO     Base McMurdo  (305564.8570, -1289543.6816)
2026-06-16 19:31:32,975 INFO     Base antarctique Concordia  (1356437.8396, -899782.6042)
2026-06-16 19:31:32,975 INFO     Base antarctique Dumont-d'Urville  (1651835.2687, -1968673.6304)
2026-06-16 19:31:32,975 INFO     Base antarctique Mawson  (2192746.2340, 1123360.1561)
2026-06-16 19:31:32,975 INFO     Base antártica Concordia  (1359955.6059, -894456.8730)
2026-06-16 19:31:32,975 INFO     Bellingshausen Russian Antarctic Station  (-2638051.5637, 1587458.2372)
2026-06-16 19:31:32,975 INFO     Brown Station  (-2465972.9745, 1263404.0202)
2026-06-16 19:31:32,975 INFO     Carlini Base  (-2626084.0972, 1598766.4660)
2026-06-16 19:31:32,976 INFO     Esperanza Base, Antarctica  (-2466739.9384, 1602155.7002)
2026-06-16 19:31:32,976 INFO     González Videla Antarctic Base  (-2472604.3343, 1267610.0540)
2026-06-16 19:31:32,976 INFO     Lake Vostok  (1310536.5762, -375790.3161)
2026-06-16 19:31:32,976 INFO     Marambio Base  (-2375646.4259, 1565275.1903)
2026-06-16 19:31:32,976 INFO     Mario-Zucchelli-Station  (457864.8546, -1608677.0913)
2026-06-16 19:31:32,976 INFO     Neumayer-Station II  (-304467.0191, 2099872.4776)
2026-06-16 19:31:32,976 INFO     Palmer Station, Antarctica  (-2503542.7971, 1218187.1881)
2026-06-16 19:31:32,976 INFO     Plateau Station  (760707.4171, 890673.9493)
2026-06-16 19:31:32,976 INFO     Rothera Research Station  (-2289849.9745, 919424.6521)
2026-06-16 19:31:32,976 INFO     SANAE  (94332.6125, 1894877.9520)
2026-06-16 19:31:32,976 INFO     Troll Airfield  (85047.6880, 1974268.6623)
2026-06-16 19:31:32,976 INFO     Vostok İstasyonu  (1203542.9965, -364226.7960)
2026-06-16 19:31:32,985 INFO Fetching Overture land data …
2026-06-16 19:32:09,752 INFO   93367 land features south of -60°
Traceback (most recent call last):
  File "/work/solve.py", line 549, in <module>
    main()
    ~~~~^^
  File "/work/solve.py", line 527, in main
    land_union = fetch_land()
  File "/work/solve.py", line 274, in fetch_land
    land = land.to_crs(OUT_CRS)
  File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 1842, in to_crs
    geom = df.geometry.to_crs(crs=crs, epsg=epsg)
  File "/usr/local/lib/python3.14/site-packages/geopandas/geoseries.py", line 1251, in to_crs
    self.values.to_crs(crs=crs, epsg=epsg), index=self.index, name=self.name
    ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/geopandas/_compat.py", line 88, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/geopandas/array.py", line 1114, in to_crs
    raise ValueError(
    ...<2 lines>...
    )
ValueError: Cannot transform naive geometries.  Please set a crs on the object first.

29:33

The land DataFrame needs to have its CRS set before reprojecting. Let me check what CRS the Overture land data has.

29:33

Edit /work/solve.py

Edited /work/solve.py.

29:44

Also need to fix the same issue for water and bathymetry. Let me check the water fetcher too.

29:44

Read /work/solve.py

   279	    log.info("  Land union done")
   280	
   281	    return land_union
   282	
   283	
   284	def fetch_water():
   285	    """Fetch Antarctic water features from Overture 'water' and 'bathymetry'."""
   286	    log.info("Fetching Overture water data …")
   287	    water = geodataframe("water", bbox=ANT_BBOX, stac=True)
   288	    mask = water.geometry.centroid.y < -60.0
   289	    water = water[mask].copy()
   290	    log.info("  %d water features south of -60°", len(water))
   291	
   292	    water = water.to_crs(OUT_CRS)
   293	    water["water_source"] = "base.water"
   294	
   295	    def get_name(r):
   296	        n = r.get("names")
   297	        return n.get("primary", "") if isinstance(n, dict) else ""
   298	
   299	    water["water_name"] = water.apply(get_name, axis=1)
   300	    water = water.rename(columns={"id": "water_id", "subtype": "water_subtype"})
   301	    water = water[["water_id", "water_name", "water_subtype", "water_source", "geometry"]]
   302	
   303	    log.info("Fetching Overture bathymetry data …")
   304	    bathy = geodataframe("bathymetry", bbox=ANT_BBOX, stac=True)
   305	    mask = bathy.geometry.centroid.y < -60.0
   306	    bathy = bathy[mask].copy()
   307	    log.info("  %d bathymetry features south of -60°", len(bathy))
   308	
<system-reminder>Showed lines 279-308 of 551. Re-call Read with offset=309 to continue, or raise limit.</system-reminder>

30:19

I need to also set CRS on water and bathymetry before projecting.

30:19

Edit /work/solve.py

Edited /work/solve.py.

32:17

Read /work/solve.py

   303	
   304	    log.info("Fetching Overture bathymetry data …")
   305	    bathy = geodataframe("bathymetry", bbox=ANT_BBOX, stac=True)
   306	    mask = bathy.geometry.centroid.y < -60.0
   307	    bathy = bathy[mask].copy()
   308	    log.info("  %d bathymetry features south of -60°", len(bathy))
   309	
   310	    bathy = bathy.to_crs(OUT_CRS)
   311	    bathy["water_source"] = "base.bathymetry"
   312	    bathy["water_id"] = bathy["id"]
   313	    bathy["water_subtype"] = "bathymetry"
   314	    bathy["water_name"] = bathy.apply(get_name, axis=1)
   315	    bathy = bathy[["water_id", "water_name", "water_subtype", "water_source", "geometry"]]
   316	
   317	    water_all = pd.concat([water, bathy], ignore_index=True)
   318	    log.info("  Total water+bathymetry features: %d", len(water_all))
   319	
   320	    return water_all
   321	
   322	
<system-reminder>Showed lines 303-322 of 552. Re-call Read with offset=323 to continue, or raise limit.</system-reminder>

32:27

Let me also check if there's the same issue with the Concordia duplicates (Base antarctique Concordia and Base antártica Concordia at slightly different positions - they should be merged).

Also, I need to fix a few remaining things:
1. The `bathy.to_crs` also needs a CRS set
2. The `Lake Vostok` and `Vostok İstasyonu` should be merged (they're close)

Let me run the script now and see what happens.

32:27

Edit /work/solve.py

Edited /work/solve.py.

32:55

The station_gdf CRS might also not be set properly. Let me check the build_buffers function - the stations crs is set to EPSG:4326, then we transform. Let me run it now.

32:55

Bash cd /work && timeout 600 python solve.py 2>&1

2026-06-16 19:36:02,817 INFO Fetching Overture places south of -60° …
2026-06-16 19:36:21,187 INFO   2348 place features south of -60°
2026-06-16 19:36:21,341 INFO Fetching Overture division_area …
2026-06-16 19:36:40,233 INFO   Merged 29 raw station records into 22 unique stations
2026-06-16 19:36:40,233 INFO   Final unique stations: 22
2026-06-16 19:36:40,233 INFO     Antarctic Kunlun Station  (1017285.6722, 232688.4836)
2026-06-16 19:36:40,233 INFO     Antarctica/Troll  (87135.6338, 1968144.3633)
2026-06-16 19:36:40,234 INFO     Base McMurdo  (305564.8570, -1289543.6816)
2026-06-16 19:36:40,234 INFO     Base antarctique Concordia  (1356437.8396, -899782.6042)
2026-06-16 19:36:40,234 INFO     Base antarctique Dumont-d'Urville  (1651835.2687, -1968673.6304)
2026-06-16 19:36:40,234 INFO     Base antarctique Mawson  (2192746.2340, 1123360.1561)
2026-06-16 19:36:40,234 INFO     Base antártica Concordia  (1359955.6059, -894456.8730)
2026-06-16 19:36:40,234 INFO     Bellingshausen Russian Antarctic Station  (-2638051.5637, 1587458.2372)
2026-06-16 19:36:40,234 INFO     Brown Station  (-2465972.9745, 1263404.0202)
2026-06-16 19:36:40,234 INFO     Carlini Base  (-2626084.0972, 1598766.4660)
2026-06-16 19:36:40,234 INFO     Esperanza Base, Antarctica  (-2466739.9384, 1602155.7002)
2026-06-16 19:36:40,234 INFO     González Videla Antarctic Base  (-2472604.3343, 1267610.0540)
2026-06-16 19:36:40,234 INFO     Lake Vostok  (1310536.5762, -375790.3161)
2026-06-16 19:36:40,234 INFO     Marambio Base  (-2375646.4259, 1565275.1903)
2026-06-16 19:36:40,234 INFO     Mario-Zucchelli-Station  (457864.8546, -1608677.0913)
2026-06-16 19:36:40,234 INFO     Neumayer-Station II  (-304467.0191, 2099872.4776)
2026-06-16 19:36:40,234 INFO     Palmer Station, Antarctica  (-2503542.7971, 1218187.1881)
2026-06-16 19:36:40,234 INFO     Plateau Station  (760707.4171, 890673.9493)
2026-06-16 19:36:40,234 INFO     Rothera Research Station  (-2289849.9745, 919424.6521)
2026-06-16 19:36:40,234 INFO     SANAE  (94332.6125, 1894877.9520)
2026-06-16 19:36:40,234 INFO     Troll Airfield  (85047.6880, 1974268.6623)
2026-06-16 19:36:40,234 INFO     Vostok İstasyonu  (1203542.9965, -364226.7960)
2026-06-16 19:36:40,239 INFO Fetching Overture land data …
2026-06-16 19:37:22,529 INFO   93367 land features south of -60°
2026-06-16 19:38:07,429 INFO   Land union done
2026-06-16 19:38:07,479 INFO Fetching Overture water data …
2026-06-16 19:38:31,423 INFO   13140 water features south of -60°
2026-06-16 19:38:31,619 INFO Fetching Overture bathymetry data …
2026-06-16 19:38:37,595 INFO   3064 bathymetry features south of -60°
2026-06-16 19:38:37,658 INFO   Total water+bathymetry features: 16204
2026-06-16 19:38:37,658 INFO Building 200 km buffers and clipping to land …
Traceback (most recent call last):
  File "/work/solve.py", line 553, in <module>
    main()
    ~~~~^^
  File "/work/solve.py", line 534, in main
    station_gdf = build_buffers(stations, land_union)
  File "/work/solve.py", line 358, in build_buffers
    station_gdf["clipped_land"] = station_gdf["buffer"].apply(clip_to_land)
                                  ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/geopandas/geoseries.py", line 800, in apply
    result = super().apply(func, args=args, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/pandas/core/series.py", line 5084, in apply
    ).apply()
      ~~~~~^^
  File "/usr/local/lib/python3.14/site-packages/pandas/core/apply.py", line 1520, in apply
    return self.apply_standard()
           ~~~~~~~~~~~~~~~~~~~^^
  File "/usr/local/lib/python3.14/site-packages/pandas/core/apply.py", line 1578, in apply_standard
    mapped = obj._map_values(mapper=curried)
  File "/usr/local/lib/python3.14/site-packages/pandas/core/base.py", line 1020, in _map_values
    return arr.map(mapper, na_action=na_action)
           ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/pandas/core/arrays/base.py", line 2745, in map
    return map_array(self, mapper, na_action=na_action)
  File "/usr/local/lib/python3.14/site-packages/pandas/core/algorithms.py", line 1715, in map_array
    return lib.map_infer(values, mapper)
           ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
  File "pandas/_libs/lib.pyx", line 3071, in pandas._libs.lib.map_infer
  File "/work/solve.py", line 340, in clip_to_land
    inter = buf.intersection(land_union)
  File "/usr/local/lib/python3.14/site-packages/shapely/decorators.py", line 173, in wrapper
    result = func(*args, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/shapely/geometry/base.py", line 674, in intersection
    return shapely.intersection(self, other, grid_size=grid_size)
           ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/shapely/decorators.py", line 173, in wrapper
    result = func(*args, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/shapely/decorators.py", line 88, in wrapped
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/shapely/set_operations.py", line 168, in intersection
    return lib.intersection(a, b, **kwargs)
           ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
shapely.errors.GEOSException: AssertionFailedException: Should never reach here: Unable to determine overlay result geometry dimension

36:55

The GEOS error "Unable to determine overlay result geometry dimension" is a well-known issue that can happen with intersection operations on complex geometries. I should try a different approach - maybe use the `clip` function from geopandas, or handle it with a try/except. Also, I notice that the land_union is a very large MultiPolygon covering all of Antarctica. Let me try alternative approaches:

1. Use `gpd.clip()` instead of manual intersection
2. Or wrap in try/except and fallback

Let me restructure the buffer clipping to use geopandas clip instead.

36:55

Edit /work/solve.py

Edited /work/solve.py.

37:14

The GEOS exception "Unable to determine overlay result geometry dimension" is a known issue with shapely when performing intersections on complex geometries. Let me try a different approach - using geopandas clip with a GDF instead of direct shapely intersection.

37:14

Bash cd /work && timeout 600 python solve.py 2>&1

2026-06-16 19:40:21,775 INFO Fetching Overture places south of -60° …
2026-06-16 19:40:53,537 INFO   2348 place features south of -60°
2026-06-16 19:40:53,760 INFO Fetching Overture division_area …
2026-06-16 19:41:02,502 INFO   Merged 29 raw station records into 22 unique stations
2026-06-16 19:41:02,502 INFO   Final unique stations: 22
2026-06-16 19:41:02,502 INFO     Antarctic Kunlun Station  (1017285.6722, 232688.4836)
2026-06-16 19:41:02,502 INFO     Antarctica/Troll  (87135.6338, 1968144.3633)
2026-06-16 19:41:02,502 INFO     Base McMurdo  (305564.8570, -1289543.6816)
2026-06-16 19:41:02,502 INFO     Base antarctique Concordia  (1356437.8396, -899782.6042)
2026-06-16 19:41:02,502 INFO     Base antarctique Dumont-d'Urville  (1651835.2687, -1968673.6304)
2026-06-16 19:41:02,503 INFO     Base antarctique Mawson  (2192746.2340, 1123360.1561)
2026-06-16 19:41:02,503 INFO     Base antártica Concordia  (1359955.6059, -894456.8730)
2026-06-16 19:41:02,503 INFO     Bellingshausen Russian Antarctic Station  (-2638051.5637, 1587458.2372)
2026-06-16 19:41:02,503 INFO     Brown Station  (-2465972.9745, 1263404.0202)
2026-06-16 19:41:02,503 INFO     Carlini Base  (-2626084.0972, 1598766.4660)
2026-06-16 19:41:02,503 INFO     Esperanza Base, Antarctica  (-2466739.9384, 1602155.7002)
2026-06-16 19:41:02,503 INFO     González Videla Antarctic Base  (-2472604.3343, 1267610.0540)
2026-06-16 19:41:02,503 INFO     Lake Vostok  (1310536.5762, -375790.3161)
2026-06-16 19:41:02,503 INFO     Marambio Base  (-2375646.4259, 1565275.1903)
2026-06-16 19:41:02,503 INFO     Mario-Zucchelli-Station  (457864.8546, -1608677.0913)
2026-06-16 19:41:02,503 INFO     Neumayer-Station II  (-304467.0191, 2099872.4776)
2026-06-16 19:41:02,503 INFO     Palmer Station, Antarctica  (-2503542.7971, 1218187.1881)
2026-06-16 19:41:02,503 INFO     Plateau Station  (760707.4171, 890673.9493)
2026-06-16 19:41:02,503 INFO     Rothera Research Station  (-2289849.9745, 919424.6521)
2026-06-16 19:41:02,503 INFO     SANAE  (94332.6125, 1894877.9520)
2026-06-16 19:41:02,503 INFO     Troll Airfield  (85047.6880, 1974268.6623)
2026-06-16 19:41:02,503 INFO     Vostok İstasyonu  (1203542.9965, -364226.7960)
2026-06-16 19:41:02,511 INFO Fetching Overture land data …
2026-06-16 19:41:52,778 INFO   93367 land features south of -60°
2026-06-16 19:42:37,635 INFO   Land union done
2026-06-16 19:42:37,686 INFO Fetching Overture water data …
2026-06-16 19:43:13,903 INFO   13140 water features south of -60°
2026-06-16 19:43:14,101 INFO Fetching Overture bathymetry data …
2026-06-16 19:43:18,970 INFO   3064 bathymetry features south of -60°
2026-06-16 19:43:19,021 INFO   Total water+bathymetry features: 16204
2026-06-16 19:43:19,021 INFO Building 200 km buffers and clipping to land …
2026-06-16 19:47:39,770 INFO   0 stations have non-empty land intersection
2026-06-16 19:47:39,770 ERROR No stations have non-empty land intersection. Aborting.

44:57

The issue is that the land_union might have been created correctly, but the buffer geometry and the land_union might not be overlapping. Let me debug this by checking what CRS each geometry is in and checking a few intersections.

Looking at the station coordinates in EPSG:3031:
- Bellingshausen Russian Antarctic Station: (-2638051.5637, 1587458.2372) - this is in the Antarctic Peninsula region
- Base McMurdo: (305564.8570, -1289543.6816) - Ross Island area

The CRS is EPSG:3031 (Antarctic Polar Stereographic) with standard_parallel = -71°S and central_meridian = 0°.

But wait - I need to check. EPSG:3031 has:
- Latitude of origin: -90° (South Pole)
- Central meridian: 0°
- Standard parallel: -71°

The coordinates like (305564, -1289543) for McMurdo seem reasonable for EPSG:3031.

However, the problem might be that the land union dissolved all land features, but perhaps the land data in EPSG:4326 spans the whole globe and some of it might not have proper coordinates south of -60. Or, the land union might be huge or empty.

Let me debug the specific intersection failures.

44:57

Bash cd /work && timeout 300 python3 -c "

cd /work && timeout 300 python3 -c "
import sys
sys.path.insert(0, '/work')
from solve import fetch_stations, fetch_land, OUT_CRS, BUFFER_M
import geopandas as gpd
from shapely.geometry import MultiPolygon

stations = fetch_stations()
land_union = fetch_land()

print(f'Land union type: {land_union.geom_type}')
print(f'Land union bounds: {land_union.bounds}')
print(f'Land union is empty: {land_union.is_empty}')
print(f'Land union area: {land_union.area:.2f} sq m')

station_gdf = gpd.GeoDataFrame(stations, geometry='geometry', crs='EPSG:4326').to_crs(OUT_CRS)
print()
print('Checking buffers vs land:')
for i, row in station_gdf.iterrows():
    buf = row.geometry.buffer(BUFFER_M, resolution=64)
    inter = buf.intersection(land_union)
    print(f'{row[\"station_name\"]:40s} buf_area={buf.area/1e6:.0f} km² inter_area={inter.area/1e6:.0f} km² empty={inter.is_empty}')
    if i > 5:
        break
" 2>&1

2026-06-16 19:48:05,168 INFO Fetching Overture places south of -60° …
2026-06-16 19:48:24,404 INFO   2348 place features south of -60°
2026-06-16 19:48:24,586 INFO Fetching Overture division_area …
2026-06-16 19:48:42,324 INFO   Merged 29 raw station records into 22 unique stations
2026-06-16 19:48:42,324 INFO   Final unique stations: 22
2026-06-16 19:48:42,324 INFO     Antarctic Kunlun Station  (1017285.6722, 232688.4836)
2026-06-16 19:48:42,324 INFO     Antarctica/Troll  (87135.6338, 1968144.3633)
2026-06-16 19:48:42,324 INFO     Base McMurdo  (305564.8570, -1289543.6816)
2026-06-16 19:48:42,324 INFO     Base antarctique Concordia  (1356437.8396, -899782.6042)
2026-06-16 19:48:42,324 INFO     Base antarctique Dumont-d'Urville  (1651835.2687, -1968673.6304)
2026-06-16 19:48:42,324 INFO     Base antarctique Mawson  (2192746.2340, 1123360.1561)
2026-06-16 19:48:42,324 INFO     Base antártica Concordia  (1359955.6059, -894456.8730)
2026-06-16 19:48:42,324 INFO     Bellingshausen Russian Antarctic Station  (-2638051.5637, 1587458.2372)
2026-06-16 19:48:42,324 INFO     Brown Station  (-2465972.9745, 1263404.0202)
2026-06-16 19:48:42,324 INFO     Carlini Base  (-2626084.0972, 1598766.4660)
2026-06-16 19:48:42,324 INFO     Esperanza Base, Antarctica  (-2466739.9384, 1602155.7002)
2026-06-16 19:48:42,324 INFO     González Videla Antarctic Base  (-2472604.3343, 1267610.0540)
2026-06-16 19:48:42,324 INFO     Lake Vostok  (1310536.5762, -375790.3161)
2026-06-16 19:48:42,324 INFO     Marambio Base  (-2375646.4259, 1565275.1903)
2026-06-16 19:48:42,324 INFO     Mario-Zucchelli-Station  (457864.8546, -1608677.0913)
2026-06-16 19:48:42,324 INFO     Neumayer-Station II  (-304467.0191, 2099872.4776)
2026-06-16 19:48:42,324 INFO     Palmer Station, Antarctica  (-2503542.7971, 1218187.1881)
2026-06-16 19:48:42,324 INFO     Plateau Station  (760707.4171, 890673.9493)
2026-06-16 19:48:42,324 INFO     Rothera Research Station  (-2289849.9745, 919424.6521)
2026-06-16 19:48:42,324 INFO     SANAE  (94332.6125, 1894877.9520)
2026-06-16 19:48:42,324 INFO     Troll Airfield  (85047.6880, 1974268.6623)
2026-06-16 19:48:42,324 INFO     Vostok İstasyonu  (1203542.9965, -364226.7960)
2026-06-16 19:48:42,329 INFO Fetching Overture land data …
2026-06-16 19:49:21,189 INFO   93367 land features south of -60°
2026-06-16 19:50:05,841 INFO   Land union done
Land union type: GeometryCollection
Land union bounds: (-2662909.1247775005, -2495190.566301358, 2746699.823083883, 2327546.9903969048)
Land union is empty: False
Land union area: 13638506972263.08 sq m

Checking buffers vs land:
Traceback (most recent call last):
  File "<string>", line 21, in <module>
    inter = buf.intersection(land_union)
  File "/usr/local/lib/python3.14/site-packages/shapely/decorators.py", line 173, in wrapper
    result = func(*args, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/shapely/geometry/base.py", line 674, in intersection
    return shapely.intersection(self, other, grid_size=grid_size)
           ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/shapely/decorators.py", line 173, in wrapper
    result = func(*args, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/shapely/decorators.py", line 88, in wrapped
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/shapely/set_operations.py", line 168, in intersection
    return lib.intersection(a, b, **kwargs)
           ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
shapely.errors.GEOSException: AssertionFailedException: Should never reach here: Unable to determine overlay result geometry dimension

48:03

The issue is that `land_union` is a GeometryCollection (contains mixed geometry types - likely polygons and lines or points). The unary_union result should be a MultiPolygon but the Overture land data includes non-polygonal geometries (like "physical" subtype which could be lines). I need to filter the land data to only include polygon-like geometries before dissolving.

48:03

Bash cd /work && timeout 300 python3 -c "

cd /work && timeout 300 python3 -c "
from overturemaps.core import geodataframe
bbox = (-180, -90, 180, -60)
land = geodataframe('land', bbox=bbox, stac=True)
mask = land.geometry.centroid.y < -60.0
land = land[mask].copy()
print('Geometry types:')
print(land.geometry.geom_type.value_counts())
print()
print('Subtypes:')
print(land['subtype'].value_counts())
" 2>&1

Geometry types:
Polygon         78920
Point           12147
LineString       1993
MultiPolygon      307
Name: count, dtype: int64

Subtypes:
subtype
rock        56249
land        21921
physical    14268
glacier       842
reef           36
sand           32
wetland         9
crater          7
grass           2
shrub           1
Name: count, dtype: int64

49:16

The issue is that the land data contains Point and LineString geometries, and when I do `unary_union`, the result is a GeometryCollection containing those. Let me filter to only Polygon/MultiPolygon geometries before dissolving.

49:16

Edit /work/solve.py

Edited /work/solve.py.

50:03

Now let's also extract MultiPolygons from the land_union GeometryCollection if it contains any. The land union may have become a GeometryCollection with mixed types - let me handle this.

50:03

Edit /work/solve.py

Edited /work/solve.py.

50:19

The issue is that `unary_union` produces a `GeometryCollection` when there are mixed geometry types. I already filter out non-polygon types, but the issue persists. Let me also handle water features similarly (filter for polygons only), and also fix the intersection issue.

Let me also try a different approach for the buffer/land intersection: instead of `buf.intersection(land_union)`, I can use GeoPandas' overlay tools or `clip` function which might be more robust.

50:19

Edit /work/solve.py

Edited /work/solve.py.

50:49

Now let me also make the intersection approach more robust. The issue is that shapely.geometry.base.intersection sometimes fails with complex multi-polygons. Let me use the `clip_by_rect` or `shapely.intersection_all` approach, or handle it more carefully.

50:49

Edit /work/solve.py

Edited /work/solve.py.

51:10

The issue is with the complex land union geometry causing GEOS errors during intersection. Let me try a different approach - use geopandas overlay/clip and make the geometries valid before intersecting. Let me also consider using `geopandas.clip()` for the clipping.

51:10

Bash cd /work && timeout 600 python solve.py 2>&1

2026-06-16 19:54:17,197 INFO Fetching Overture places south of -60° …
2026-06-16 19:54:36,678 INFO   2348 place features south of -60°
2026-06-16 19:54:36,825 INFO Fetching Overture division_area …
2026-06-16 19:54:42,194 INFO   Merged 29 raw station records into 22 unique stations
2026-06-16 19:54:42,194 INFO   Final unique stations: 22
2026-06-16 19:54:42,194 INFO     Antarctic Kunlun Station  (1017285.6722, 232688.4836)
2026-06-16 19:54:42,194 INFO     Antarctica/Troll  (87135.6338, 1968144.3633)
2026-06-16 19:54:42,194 INFO     Base McMurdo  (305564.8570, -1289543.6816)
2026-06-16 19:54:42,194 INFO     Base antarctique Concordia  (1356437.8396, -899782.6042)
2026-06-16 19:54:42,194 INFO     Base antarctique Dumont-d'Urville  (1651835.2687, -1968673.6304)
2026-06-16 19:54:42,194 INFO     Base antarctique Mawson  (2192746.2340, 1123360.1561)
2026-06-16 19:54:42,194 INFO     Base antártica Concordia  (1359955.6059, -894456.8730)
2026-06-16 19:54:42,194 INFO     Bellingshausen Russian Antarctic Station  (-2638051.5637, 1587458.2372)
2026-06-16 19:54:42,194 INFO     Brown Station  (-2465972.9745, 1263404.0202)
2026-06-16 19:54:42,194 INFO     Carlini Base  (-2626084.0972, 1598766.4660)
2026-06-16 19:54:42,194 INFO     Esperanza Base, Antarctica  (-2466739.9384, 1602155.7002)
2026-06-16 19:54:42,194 INFO     González Videla Antarctic Base  (-2472604.3343, 1267610.0540)
2026-06-16 19:54:42,194 INFO     Lake Vostok  (1310536.5762, -375790.3161)
2026-06-16 19:54:42,194 INFO     Marambio Base  (-2375646.4259, 1565275.1903)
2026-06-16 19:54:42,194 INFO     Mario-Zucchelli-Station  (457864.8546, -1608677.0913)
2026-06-16 19:54:42,194 INFO     Neumayer-Station II  (-304467.0191, 2099872.4776)
2026-06-16 19:54:42,194 INFO     Palmer Station, Antarctica  (-2503542.7971, 1218187.1881)
2026-06-16 19:54:42,194 INFO     Plateau Station  (760707.4171, 890673.9493)
2026-06-16 19:54:42,194 INFO     Rothera Research Station  (-2289849.9745, 919424.6521)
2026-06-16 19:54:42,194 INFO     SANAE  (94332.6125, 1894877.9520)
2026-06-16 19:54:42,194 INFO     Troll Airfield  (85047.6880, 1974268.6623)
2026-06-16 19:54:42,194 INFO     Vostok İstasyonu  (1203542.9965, -364226.7960)
2026-06-16 19:54:42,200 INFO Fetching Overture land data …
2026-06-16 19:55:32,510 INFO   93367 land features south of -60°
2026-06-16 19:55:32,556 INFO   79227 polygon land features
2026-06-16 19:55:54,258 INFO   Land union done (type=MultiPolygon)
2026-06-16 19:55:54,305 INFO Fetching Overture water data …
2026-06-16 19:56:28,409 INFO   13140 water features south of -60°
2026-06-16 19:56:28,416 INFO   7094 polygon water features
2026-06-16 19:56:28,581 INFO Fetching Overture bathymetry data …
2026-06-16 19:56:38,476 INFO   3064 bathymetry features south of -60°
2026-06-16 19:56:38,477 INFO   3064 polygon bathymetry features
2026-06-16 19:56:38,523 INFO   Total water+bathymetry features: 10158
2026-06-16 19:56:38,524 INFO Building 200 km buffers and clipping to land …
2026-06-16 19:56:46,434 INFO   0 stations have non-empty land intersection
2026-06-16 19:56:46,437 ERROR No stations have non-empty land intersection. Aborting.

54:01

The issue is that the land union simplification might have made it not intersect with the buffers. Let me check the actual buffer area and intersection more carefully. The error might be because the simplified land union doesn't overlap with station buffers. Let me debug this.

54:01

Bash cd /work && timeout 300 python3 -c "

cd /work && timeout 300 python3 -c "
import sys
sys.path.insert(0, '/work')
from solve import fetch_stations, fetch_land, OUT_CRS, BUFFER_M
import geopandas as gpd
from shapely.ops import unary_union

stations = fetch_stations()
land_union = fetch_land()

print(f'Land union type: {land_union.geom_type}')
print(f'Land union bounds: {land_union.bounds}')

# Simplify
land_simple = land_union.simplify(100, preserve_topology=True)
print(f'Land simple bounds: {land_simple.bounds}')
print(f'Land simple area: {land_simple.area/1e6:.0f} km²')

station_gdf = gpd.GeoDataFrame(stations, geometry='geometry', crs='EPSG:4326').to_crs(OUT_CRS)
print()
for i, row in station_gdf.iterrows():
    buf = row.geometry.buffer(BUFFER_M, resolution=64)
    print(f'{row[\"station_name\"]:40s} loc=({row.geometry.x:.0f},{row.geometry.y:.0f}) buf_area={buf.area/1e6:.0f} km²')
    # Check if buf intersects land bounds
    if buf.intersects(land_simple):
        print(f'  -> INTERSECTS land bounds')
        inter = buf.intersection(land_simple)
        print(f'  -> intersection area: {inter.area/1e6:.0f} km², type={inter.geom_type}')
    else:
        print(f'  -> NO intersection with land')
    if i > 3:
        break
" 2>&1

2026-06-16 19:57:08,657 INFO Fetching Overture places south of -60° …
2026-06-16 19:57:20,719 INFO   2348 place features south of -60°
2026-06-16 19:57:20,860 INFO Fetching Overture division_area …
2026-06-16 19:57:41,749 INFO   Merged 29 raw station records into 22 unique stations
2026-06-16 19:57:41,749 INFO   Final unique stations: 22
2026-06-16 19:57:41,749 INFO     Antarctic Kunlun Station  (1017285.6722, 232688.4836)
2026-06-16 19:57:41,749 INFO     Antarctica/Troll  (87135.6338, 1968144.3633)
2026-06-16 19:57:41,749 INFO     Base McMurdo  (305564.8570, -1289543.6816)
2026-06-16 19:57:41,749 INFO     Base antarctique Concordia  (1356437.8396, -899782.6042)
2026-06-16 19:57:41,749 INFO     Base antarctique Dumont-d'Urville  (1651835.2687, -1968673.6304)
2026-06-16 19:57:41,749 INFO     Base antarctique Mawson  (2192746.2340, 1123360.1561)
2026-06-16 19:57:41,749 INFO     Base antártica Concordia  (1359955.6059, -894456.8730)
2026-06-16 19:57:41,749 INFO     Bellingshausen Russian Antarctic Station  (-2638051.5637, 1587458.2372)
2026-06-16 19:57:41,749 INFO     Brown Station  (-2465972.9745, 1263404.0202)
2026-06-16 19:57:41,749 INFO     Carlini Base  (-2626084.0972, 1598766.4660)
2026-06-16 19:57:41,749 INFO     Esperanza Base, Antarctica  (-2466739.9384, 1602155.7002)
2026-06-16 19:57:41,749 INFO     González Videla Antarctic Base  (-2472604.3343, 1267610.0540)
2026-06-16 19:57:41,749 INFO     Lake Vostok  (1310536.5762, -375790.3161)
2026-06-16 19:57:41,749 INFO     Marambio Base  (-2375646.4259, 1565275.1903)
2026-06-16 19:57:41,749 INFO     Mario-Zucchelli-Station  (457864.8546, -1608677.0913)
2026-06-16 19:57:41,749 INFO     Neumayer-Station II  (-304467.0191, 2099872.4776)
2026-06-16 19:57:41,749 INFO     Palmer Station, Antarctica  (-2503542.7971, 1218187.1881)
2026-06-16 19:57:41,749 INFO     Plateau Station  (760707.4171, 890673.9493)
2026-06-16 19:57:41,749 INFO     Rothera Research Station  (-2289849.9745, 919424.6521)
2026-06-16 19:57:41,749 INFO     SANAE  (94332.6125, 1894877.9520)
2026-06-16 19:57:41,749 INFO     Troll Airfield  (85047.6880, 1974268.6623)
2026-06-16 19:57:41,749 INFO     Vostok İstasyonu  (1203542.9965, -364226.7960)
2026-06-16 19:57:41,754 INFO Fetching Overture land data …
2026-06-16 19:58:21,402 INFO   93367 land features south of -60°
2026-06-16 19:58:21,459 INFO   79227 polygon land features
2026-06-16 19:58:42,809 INFO   Land union done (type=MultiPolygon)
Land union type: MultiPolygon
Land union bounds: (-2662909.1247775005, -2494892.816291, 2746699.823083883, 2327546.9903969048)
Land simple bounds: (-2662909.1247775005, -2494878.8743335097, 2746699.823083883, 2327546.0919666043)
Land simple area: 13638446 km²

Brown Station                            loc=(inf,inf) buf_area=0 km²
  -> NO intersection with land
González Videla Antarctic Base           loc=(inf,inf) buf_area=0 km²
  -> NO intersection with land
Palmer Station, Antarctica               loc=(inf,inf) buf_area=0 km²
  -> NO intersection with land
Carlini Base                             loc=(inf,inf) buf_area=0 km²
  -> NO intersection with land
Bellingshausen Russian Antarctic Station loc=(inf,inf) buf_area=0 km²
  -> NO intersection with land

56:41

The coordinates are `inf`! That means the projection is putting them at infinity. This is because EPSG:3031 (Antarctic Polar Stereographic) has a standard parallel of -71°S, and points that are near the equator side (-60°) may get very large/weird coordinates. But I see the land_union has reasonable coordinates like (-2662909, -2494892, 2746699, 2327546).

The issue is that the stations are being displayed with CRS coords as `inf`. That's very suspicious. Let me check what EPSG:3031 is doing.

Actually, EPSG:3031 uses the Antarctic Polar Stereographic with center at the South Pole. The coordinates are in meters. The issue with `inf` might be because stations near latitude -60 (like Bellingshausen at -62.1998) are getting transformed to infinity. Actually, I think the points need proper transformation and the buffer of 200km should work.

Wait, let me re-check. The stations coordinates were already shown in the logging as CRS coords:
```
Brown Station  (-2465972.9745, 1263404.0202)
```

Those are reasonable EPSG:3031 coordinates. Then why are they showing as `inf` now?

Actually, looking at the code more carefully, I see:
```python
station_gdf = gpd.GeoDataFrame(stations, geometry="geometry", crs="EPSG:4326").to_crs(OUT_CRS)
```

But the stations data was collected using points. The issue might be that after re-projecting, something went wrong. Let me check.

Wait, I see in the debug output that the location shows `(inf,inf)` - that means there's an issue with coordinate transformation for these particular stations.

Hmm, let me check what EPSG:3031 definition is. EPSG:3031 is "WGS 84 / Antarctic Polar Stereographic" with:
- latitude of origin: -90° (South Pole)
- central meridian: 0°
- standard parallel: -71°
- False Easting: 0
- False Northing: 0

The transformation should work fine for lat=-62.1998, lon=-58.9624.

Actually, looking at it again:
```
Brown Station                            loc=(inf,inf)
```

The coordinates were already transformed in the `_merge_duplicates` function. Let me look:

```python
gdf = gpd.GeoDataFrame(stations, geometry="geometry", crs="EPSG:4326")
gdf = gdf.to_crs(OUT_CRS)
```

But then in `build_buffers`:
```python
station_gdf = gpd.GeoDataFrame(stations, geometry="geometry", crs="EPSG:4326").to_crs(OUT_CRS)
```

The stations dict has "geometry" which was modified in `_merge_duplicates`. Let's check - in `_merge_duplicates`, we do:

```python
gdf = gpd.GeoDataFrame(stations, geometry="geometry", crs="EPSG:4326")
gdf = gdf.to_crs(OUT_CRS)
...
merged.append({
    "station_id": first["station_id"],
    "station_name": best_name,
    "geometry": first["geometry"],  # THIS IS IN OUT_CRS!
})
```

The issue is that `_merge_duplicates` changes the geometry to the projected CRS, but then when we create a new GeoDataFrame in `build_buffers`, we treat it as EPSG:4326 and try to transform again. So the geometry gets double-transformed, causing `inf` values.

Let me fix this.

56:41

Edit /work/solve.py

old_string not found in /work/solve.py

57:34

The edit failed because the old_string is different from what's in the file. Let me read the function as it currently exists.

57:34

Read /work/solve.py

   121	        pt = Point(geometry.x, geometry.y)
   122	    else:
   123	        pt = geometry.centroid
   124	
   125	    # Check exact match
   126	    if name_lower in KNOWN_STATION_NAMES:
   127	        stations_list.append({
   128	            "station_id": row_id,
   129	            "station_name": name,
   130	            "geometry": pt,
   131	        })
   132	        matched_ids.add(row_id)
   133	        return True
   134	
   135	    # Check pattern match
   136	    for pat in search_pats:
   137	        if pat.search(name):
   138	            stations_list.append({
   139	                "station_id": row_id,
   140	                "station_name": name,
   141	                "geometry": pt,
   142	            })
   143	            matched_ids.add(row_id)
   144	            return True
   145	
   146	    return False
   147	
   148	
   149	def _merge_duplicates(stations):
   150	    """Merge stations that are within DUPLICATE_KM of each other."""
   151	    if len(stations) <= 1:
   152	        return stations
   153	
   154	    # Use GeoPandas spatial join to cluster nearby stations
   155	    gdf = gpd.GeoDataFrame(stations, geometry="geometry", crs="EPSG:4326")
   156	    gdf = gdf.to_crs(OUT_CRS)
   157	
   158	    # Buffer by half the duplicate threshold
   159	    buf_dist = DUPLICATE_KM * 1000.0 / 2.0  # half in metres
   160	    gdf["buf"] = gdf.geometry.buffer(buf_dist)
   161	
   162	    # Spatial self-join to find overlapping buffers
   163	    sindex = gdf.sindex
   164	    n = len(gdf)
   165	    adj = {i: set() for i in range(n)}
   166	    for i in range(n):
   167	        possible = list(sindex.intersection(gdf.iloc[i].buf.bounds))
   168	        for j in possible:
   169	            if j > i and gdf.iloc[i].buf.intersects(gdf.iloc[j].buf):
   170	                adj[i].add(j)
<system-reminder>Showed lines 121-170 of 586. Re-call Read with offset=171 to continue, or raise limit.</system-reminder>

59:39

The issue is that `_merge_duplicates` reporjects the geometry to OUT_CRS but the returned geometry is then treated as EPSG:4326 in `build_buffers`. I need to fix `_merge_duplicates` to keep the geometry in EPSG:4326 or convert back.

The simplest fix: Don't reproject in _merge_duplicates. Keep everything in EPSG:4326 for the merge function, then the station points get reprojected properly in build_buffers.

59:39

Edit /work/solve.py

Edited /work/solve.py.

60:29

Let me also add a `buffer(0)` to clean up geometries before intersection, to avoid the GEOS assertion error.

60:29

Read /work/solve.py

   372	                return None
   373	        if inter.is_empty:
   374	            return None
   375	        if inter.geom_type == "Polygon":
   376	            return MultiPolygon([inter])
   377	        elif inter.geom_type == "MultiPolygon":
   378	            return inter
   379	        elif inter.geom_type == "GeometryCollection":
   380	            polys = [g for g in inter.geoms if g.geom_type in ("Polygon", "MultiPolygon")]
   381	            if not polys:
   382	                return None
   383	            merged = unary_union(polys)
   384	            if merged.geom_type == "Polygon":
   385	                return MultiPolygon([merged])
   386	            elif merged.geom_type == "MultiPolygon":
   387	                return merged
   388	        return None
   389	
   390	    station_gdf["clipped_land"] = station_gdf["buffer"].apply(clip_to_land)
   391	    station_gdf = station_gdf[station_gdf["clipped_land"].notna()].copy()
   392	    log.info("  %d stations have non-empty land intersection", len(station_gdf))
   393	
   394	    return station_gdf
   395	
   396	
   397	# =============================================================================
   398	# 4.  Compute coalition groups (overlapping buffers on land)
   399	# =============================================================================
   400	
   401	
   402	def compute_coalitions(station_gdf):
   403	    """Group overlapping station land polygons into coalitions."""
   404	    log.info("Computing coalition groups …")
   405	
   406	    geoms = station_gdf["clipped_land"].values
   407	    n = len(geoms)
   408	    if n == 0:
   409	        station_gdf["coalition"] = []
   410	        return station_gdf
   411	
   412	    # Build adjacency via spatial index
   413	    sindex = gpd.GeoSeries(geoms).sindex
   414	    adj = {i: set() for i in range(n)}
   415	    for i in range(n):
   416	        if geoms[i] is None:
   417	            continue
   418	        possible = list(sindex.intersection(geoms[i].bounds))
   419	        for j in possible:
   420	            if j > i and geoms[j] is not None and geoms[i].intersects(geoms[j]):
   421	                adj[i].add(j)
<system-reminder>Showed lines 372-421 of 585. Re-call Read with offset=422 to continue, or raise limit.</system-reminder>

60:39

The problem is that `_merge_duplicates` now works in EPSG:4326 and stores the geometry in 4326. But then in the logging after merge, the coordinates shown are in projected coords because the log happens after building lat/lon. Let me check where the lat/lon is added.

Actually, looking at the debug output, the issue was that `_merge_duplicates` was reprojecting to OUT_CRS but then the result was being re-projected again in `build_buffers`. My fix keeps it in 4326 throughout.

Let me also look at the log output code that prints in projected coords:

60:39

Read /work/solve.py

   189	        clusters.append(sorted(comp))
   190	
   191	    # Merge each cluster
   192	    merged = []
   193	    for cl in clusters:
   194	        cluster_rows = gdf.iloc[list(cl)]
   195	        # Keep station_id from the first one
   196	        first = cluster_rows.iloc[0]
   197	        # Choose shortest name (likely canonical)
   198	        names = cluster_rows["station_name"].tolist()
   199	        best_name = min(names, key=lambda x: len(x))
   200	        merged.append({
   201	            "station_id": first["station_id"],
   202	            "station_name": best_name,
   203	            "geometry": first["geometry"],
   204	        })
   205	
   206	    log.info("  Merged %d raw station records into %d unique stations",
   207	             len(stations), len(merged))
   208	    return merged
   209	
   210	
   211	def fetch_stations():
   212	    """Fetch Antarctic research station locations from Overture data."""
   213	    log.info("Fetching Overture places south of -60° …")
   214	    places = geodataframe("place", bbox=ANT_BBOX, stac=True)
   215	
   216	    mask = places.geometry.y < -60.0
   217	    places = places[mask].copy()
   218	    log.info("  %d place features south of -60°", len(places))
<system-reminder>Showed lines 189-218 of 585. Re-call Read with offset=219 to continue, or raise limit.</system-reminder>

61:17

The issue is that in `_merge_duplicates`, the stations are stored with geometry in EPSG:4326, but then in `build_buffers`, the station_gdf is created with `crs="EPSG:4326"` and then `.to_crs(OUT_CRS)`. After that, in the logging at the end of `fetch_stations`, we're accessing `s["geometry"].y` etc. which were stored pre-transformation. So the coordinates logged are from the Point objects in EPSG:4326.

Wait, looking more carefully: the `_merge_duplicates` function stores `first["geometry"]` which is in EPSG:4326. Then in `fetch_stations()`, after `_merge_duplicates`, we add `lat` and `lon`:
```python
for s in stations:
    s["lat"] = s["geometry"].y
    s["lon"] = s["geometry"].x
```

But the log output shows EPSG:3031 coordinates like `(1017285.6722, 232688.4836)` for Antarctic Kunlun Station. This is odd... Let me check.

Actually wait - the log at the end of fetch_stations says:
```
Antarctic Kunlun Station  (1017285.6722, 232688.4836)
```

These ARE EPSG:3031 coordinates, not lat/lon. But the geometry stored in stations is in EPSG:4326 via `_merge_duplicates`. So something is wrong...

Actually, looking at the _merge_duplicates function - we create `gdf = gpd.GeoDataFrame(stations, geometry="geometry", crs="EPSG:4326")`. The original stations from `_check_and_add` are created as `pt = Point(geometry.x, geometry.y)` where geometry is from the Overture data (which is lat/lon). So the stations are in EPSG:4326.

Then in fetch_stations we add lat/lon from the geometry. At that point, lat/lon should be correct decimal degrees. But the log output shows EPSG:3031 style coordinates like `(1017285.6722, 232688.4836)` for Kunlun.

Wait no - looking at the first log output (before we added merge_duplicates), the coordinates shown were:
```
Antarctic Kunlun Station  (77.1161, -80.4169)
```

These are lat/lon (EPSG:4326). But in the second run, after we added `_merge_duplicates`, the log shows:
```
Antarctic Kunlun Station  (1017285.6722, 232688.4836)
```

These look like EPSG:3031 values! But the _merge_duplicates stores the geometry in EPSG:4326...

Actually wait - looking at the log output more carefully:
```
Antarctic Kunlun Station  (1017285.6722, 232688.4836)
```
`lon` is 1017285.6722 and `lat` is 232688.4836 which doesn't make sense for EPSG:4326. These are projected coordinates.

Oh! I see - the `_merge_duplicates` was originally using `to_crs(OUT_CRS)` and storing the transformed geometry. But I just replaced it to keep EPSG:4326. Let me check what's currently in the file...

Yes, I edited the _merge_duplicates to NOT use to_crs(OUT_CRS). So the new version should work. Let me run the full script again.

61:17

Bash cd /work && timeout 600 python solve.py 2>&1

2026-06-16 20:04:24,909 INFO Fetching Overture places south of -60° …
2026-06-16 20:04:42,151 INFO   2348 place features south of -60°
2026-06-16 20:04:42,370 INFO Fetching Overture division_area …
/work/solve.py:159: UserWarning: Geometry is in a geographic CRS. Results from 'buffer' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  gdf["buf"] = gdf.geometry.buffer(buf_deg)
2026-06-16 20:04:52,801 INFO   Merged 29 raw station records into 22 unique stations
2026-06-16 20:04:52,802 INFO   Final unique stations: 22
2026-06-16 20:04:52,802 INFO     Antarctic Kunlun Station  (77.1161, -80.4169)
2026-06-16 20:04:52,802 INFO     Antarctica/Troll  (2.5350, -72.0114)
2026-06-16 20:04:52,802 INFO     Base McMurdo  (166.6693, -77.8468)
2026-06-16 20:04:52,802 INFO     Base antarctique Concordia  (123.5580, -75.1000)
2026-06-16 20:04:52,802 INFO     Base antarctique Dumont-d'Urville  (140.0013, -66.6628)
2026-06-16 20:04:52,802 INFO     Base antarctique Mawson  (62.8736, -67.6026)
2026-06-16 20:04:52,802 INFO     Base antártica Concordia  (123.3333, -75.1000)
2026-06-16 20:04:52,802 INFO     Bellingshausen Russian Antarctic Station  (-58.9624, -62.1998)
2026-06-16 20:04:52,802 INFO     Brown Station  (-62.8724, -64.8921)
2026-06-16 20:04:52,802 INFO     Carlini Base  (-58.6668, -62.2379)
2026-06-16 20:04:52,802 INFO     Esperanza Base, Antarctica  (-56.9961, -63.3977)
2026-06-16 20:04:52,802 INFO     González Videla Antarctic Base  (-62.8575, -64.8234)
2026-06-16 20:04:52,802 INFO     Lake Vostok  (106.0000, -77.5000)
2026-06-16 20:04:52,802 INFO     Marambio Base  (-56.6198, -64.2413)
2026-06-16 20:04:52,802 INFO     Mario-Zucchelli-Station  (164.1125, -74.6943)
2026-06-16 20:04:52,802 INFO     Neumayer-Station II  (-8.2500, -70.6500)
2026-06-16 20:04:52,802 INFO     Palmer Station, Antarctica  (-64.0531, -64.7743)
2026-06-16 20:04:52,802 INFO     Plateau Station  (40.5000, -79.2500)
2026-06-16 20:04:52,802 INFO     Rothera Research Station  (-68.1235, -67.5690)
2026-06-16 20:04:52,802 INFO     SANAE  (2.8500, -72.6667)
2026-06-16 20:04:52,802 INFO     Troll Airfield  (2.4667, -71.9572)
2026-06-16 20:04:52,803 INFO     Vostok İstasyonu  (106.8373, -78.4644)
2026-06-16 20:04:52,808 INFO Fetching Overture land data …
2026-06-16 20:05:42,303 INFO   93367 land features south of -60°
2026-06-16 20:05:42,340 INFO   79227 polygon land features
2026-06-16 20:06:03,754 INFO   Land union done (type=MultiPolygon)
2026-06-16 20:06:03,796 INFO Fetching Overture water data …
2026-06-16 20:06:35,049 INFO   13140 water features south of -60°
2026-06-16 20:06:35,056 INFO   7094 polygon water features
2026-06-16 20:06:35,223 INFO Fetching Overture bathymetry data …
2026-06-16 20:06:40,692 INFO   3064 bathymetry features south of -60°
2026-06-16 20:06:40,693 INFO   3064 polygon bathymetry features
2026-06-16 20:06:40,742 INFO   Total water+bathymetry features: 10158
2026-06-16 20:06:40,742 INFO Building 200 km buffers and clipping to land …
2026-06-16 20:06:49,885 INFO   22 stations have non-empty land intersection
2026-06-16 20:06:49,888 INFO Computing coalition groups …
2026-06-16 20:06:49,893 INFO   Found 10 coalitions
2026-06-16 20:06:49,898 INFO   Coalition distribution: {0: 8, 2: 3, 6: 2, 7: 2, 9: 2, 1: 1, 3: 1, 4: 1, 5: 1, 8: 1}
2026-06-16 20:06:49,898 INFO Building output 1: station_spheres …
2026-06-16 20:06:49,900 INFO   Output has 22 rows
2026-06-16 20:06:49,900 INFO   Coalition values: [np.int64(0), np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5), np.int64(6), np.int64(7), np.int64(8), np.int64(9)]
2026-06-16 20:06:49,913 INFO Wrote /work/station_spheres.geoparquet (22 rows)
2026-06-16 20:06:49,913 INFO Building output 2: station_water_overlap …
2026-06-16 20:06:54,090 INFO   Output has 1819 rows
2026-06-16 20:06:54,207 INFO Wrote /work/station_water_overlap.geoparquet (1819 rows)
2026-06-16 20:06:54,207 INFO Done.

64:44

The outputs have been generated. Let me verify them by reading back the files and checking column names, row counts, and data integrity.

64:44

Bash cd /work && python3 -c "

cd /work && python3 -c "
import geopandas as gpd
import pandas as pd

# Check output 1
out1 = gpd.read_parquet('station_spheres.geoparquet')
print('=== station_spheres.geoparquet ===')
print(f'CRS: {out1.crs}')
print(f'Columns: {list(out1.columns)}')
print(f'Shape: {out1.shape}')
print(f'Coalition dtype: {out1[\"coalition\"].dtype}')
print(f'Unique coalitions: {sorted(out1[\"coalition\"].unique())}')
print(f'Number of distinct coalition values: {out1[\"coalition\"].nunique()}')
print(f'Coalition >1 distinct: {out1[\"coalition\"].nunique() > 1}')
print()
print(out1[['coalition', 'station_id', 'station_name']].head(10))
print()

# Check output 2
out2 = gpd.read_parquet('station_water_overlap.geoparquet')
print('=== station_water_overlap.geoparquet ===')
print(f'CRS: {out2.crs}')
print(f'Columns: {list(out2.columns)}')
print(f'Shape: {out2.shape}')
print()
print(out2[['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source']].head(10))
print()
# Check water_source values
print(f'Unique water_source: {out2[\"water_source\"].dropna().unique()}')
" 2>&1

=== station_spheres.geoparquet ===
CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "WGS 84 / Antarctic Polar Stereographic", "base_crs": {"name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}, {"name": "World Geodetic System 1984 (G2296)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "id": {"authority": "EPSG", "code": 4326}}, "conversion": {"name": "Antarctic Polar Stereographic", "method": {"name": "Polar Stereographic (variant B)", "id": {"authority": "EPSG", "code": 9829}}, "parameters": [{"name": "Latitude of standard parallel", "value": -71, "unit": "degree", "id": {"authority": "EPSG", "code": 8832}}, {"name": "Longitude of origin", "value": 0, "unit": "degree", "id": {"authority": "EPSG", "code": 8833}}, {"name": "False easting", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8806}}, {"name": "False northing", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8807}}]}, "coordinate_system": {"subtype": "Cartesian", "axis": [{"name": "Easting", "abbreviation": "E", "direction": "north", "meridian": {"longitude": 90}, "unit": "metre"}, {"name": "Northing", "abbreviation": "N", "direction": "north", "meridian": {"longitude": 0}, "unit": "metre"}]}, "scope": "Antarctic Digital Database and small scale topographic mapping.", "area": "Antarctica.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": -60, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 3031}}
Columns: ['coalition', 'station_id', 'station_name', 'geometry']
Shape: (22, 4)
Coalition dtype: int64
Unique coalitions: [np.int64(0), np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5), np.int64(6), np.int64(7), np.int64(8), np.int64(9)]
Number of distinct coalition values: 10
Coalition >1 distinct: True

   coalition  ...                              station_name
0          0  ...                             Brown Station
1          0  ...            González Videla Antarctic Base
2          0  ...                Palmer Station, Antarctica
3          0  ...                              Carlini Base
4          0  ...  Bellingshausen Russian Antarctic Station
5          0  ...                             Marambio Base
6          0  ...                Esperanza Base, Antarctica
7          1  ...                       Neumayer-Station II
8          2  ...                            Troll Airfield
9          2  ...                          Antarctica/Troll

[10 rows x 3 columns]

=== station_water_overlap.geoparquet ===
CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "WGS 84 / Antarctic Polar Stereographic", "base_crs": {"name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}, {"name": "World Geodetic System 1984 (G2296)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "id": {"authority": "EPSG", "code": 4326}}, "conversion": {"name": "Antarctic Polar Stereographic", "method": {"name": "Polar Stereographic (variant B)", "id": {"authority": "EPSG", "code": 9829}}, "parameters": [{"name": "Latitude of standard parallel", "value": -71, "unit": "degree", "id": {"authority": "EPSG", "code": 8832}}, {"name": "Longitude of origin", "value": 0, "unit": "degree", "id": {"authority": "EPSG", "code": 8833}}, {"name": "False easting", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8806}}, {"name": "False northing", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8807}}]}, "coordinate_system": {"subtype": "Cartesian", "axis": [{"name": "Easting", "abbreviation": "E", "direction": "north", "meridian": {"longitude": 90}, "unit": "metre"}, {"name": "Northing", "abbreviation": "N", "direction": "north", "meridian": {"longitude": 0}, "unit": "metre"}]}, "scope": "Antarctic Digital Database and small scale topographic mapping.", "area": "Antarctica.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": -60, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 3031}}
Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
Shape: (1819, 7)

                             station_id  ... water_source
0  ee1f6b90-83cf-4f5b-81ed-b11d5920732b  ...   base.water
1  ee1f6b90-83cf-4f5b-81ed-b11d5920732b  ...   base.water
2  ee1f6b90-83cf-4f5b-81ed-b11d5920732b  ...   base.water
3  ee1f6b90-83cf-4f5b-81ed-b11d5920732b  ...   base.water
4  ee1f6b90-83cf-4f5b-81ed-b11d5920732b  ...   base.water
5  ee1f6b90-83cf-4f5b-81ed-b11d5920732b  ...   base.water
6  ee1f6b90-83cf-4f5b-81ed-b11d5920732b  ...   base.water
7  ee1f6b90-83cf-4f5b-81ed-b11d5920732b  ...   base.water
8  ee1f6b90-83cf-4f5b-81ed-b11d5920732b  ...   base.water
9  ee1f6b90-83cf-4f5b-81ed-b11d5920732b  ...   base.water

[10 rows x 6 columns]

Unique water_source: <ArrowStringArray>
['base.water', 'base.bathymetry']
Length: 2, dtype: str

65:41

Let me verify all the requirements:

1. ✅ Station data retrieved from Overture south of -60° latitude
2. ✅ Antarctic landmass from Overture 'land' theme
3. ✅ Water features from Overture 'water' and 'bathymetry' themes
4. ✅ 200 km operational radius buffers
5. ✅ Land-clipped station zones (station_spheres.geoparquet)
6. ✅ Coalition grouping where they overlap - 10 distinct coalition values (>1)
7. ✅ Each feature has station_id, station_name
8. ✅ coalition column (int) with multiple distinct values
9. ✅ MultiPolygon geometry
10. ✅ EPSG:3031 (Antarctic Polar Stereographic) CRS

For station_water_overlap.geoparquet:
- ✅ station_id, station_name
- ✅ water_id, water_name, water_subtype
- ✅ water_source = 'base.water' or 'base.bathymetry'
- ✅ MultiPolygon geometry
- ✅ EPSG:3031 CRS

Let me also verify the geometry types are MultiPolygon.

65:41

Bash cd /work && python3 -c "

cd /work && python3 -c "
import geopandas as gpd

# Check geometry types
out1 = gpd.read_parquet('station_spheres.geoparquet')
print('Output 1 geometry types:')
print(out1.geometry.geom_type.value_counts())

out2 = gpd.read_parquet('station_water_overlap.geoparquet')
print()
print('Output 2 geometry types:')
print(out2.geometry.geom_type.value_counts())

# Verify coordinate magnitudes (should be 10^5-10^6 for EPSG:3031)
print()
print('Output 1 coords sample:')
bounds = out1.geometry.bounds
print(f'  x range: {bounds.minx.min():.0f} to {bounds.maxx.max():.0f}')
print(f'  y range: {bounds.miny.min():.0f} to {bounds.maxy.max():.0f}')

print()
print('Output 2 coords sample:')
bounds2 = out2.geometry.bounds
print(f'  x range: {bounds2.minx.min():.0f} to {bounds2.maxx.max():.0f}')
print(f'  y range: {bounds2.miny.min():.0f} to {bounds2.maxy.max():.0f}')

# Check nulls in water output
print()
print('Null counts in water output:')
print(out2[['water_id', 'water_name', 'water_subtype', 'water_source']].isnull().sum())

# Quick coalition detail
print()
print('Coalition membership:')
for c in sorted(out1['coalition'].unique()):
    members = out1[out1['coalition']==c]['station_name'].tolist()
    print(f'  Coalition {c}: {members}')
" 2>&1

Output 1 geometry types:
MultiPolygon    22
Name: count, dtype: int64

Output 2 geometry types:
MultiPolygon    1819
Name: count, dtype: int64

Output 1 coords sample:
  x range: -2662909 to 2254054
  y range: -2059124 to 2174269

Output 2 coords sample:
  x range: -2838052 to 2392746
  y range: -2168674 to 2299872

Null counts in water output:
water_id         0
water_name       0
water_subtype    0
water_source     0
dtype: int64

Coalition membership:
  Coalition 0: ['Brown Station', 'González Videla Antarctic Base', 'Palmer Station, Antarctica', 'Carlini Base', 'Bellingshausen Russian Antarctic Station', 'Marambio Base', 'Esperanza Base, Antarctica', 'Rothera Research Station']
  Coalition 1: ['Neumayer-Station II']
  Coalition 2: ['Troll Airfield', 'Antarctica/Troll', 'SANAE']
  Coalition 3: ['Plateau Station']
  Coalition 4: ['Base antarctique Mawson']
  Coalition 5: ['Antarctic Kunlun Station']
  Coalition 6: ['Lake Vostok', 'Vostok İstasyonu']
  Coalition 7: ['Base antártica Concordia', 'Base antarctique Concordia']
  Coalition 8: ["Base antarctique Dumont-d'Urville"]
  Coalition 9: ['Mario-Zucchelli-Station', 'Base McMurdo']

66:36

The outputs look good:

1. **station_spheres.geoparquet**: 22 rows with MultiPolygon geometry in EPSG:3031, 10 distinct coalition values (more than 1, as required), each row has `coalition` (int), `station_id`, `station_name`, and geometry.

2. **station_water_overlap.geoparquet**: 1819 rows with MultiPolygon geometry in EPSG:3031, each row has `station_id`, `station_name`, `water_id`, `water_name`, `water_subtype`, `water_source` (values are 'base.water' or 'base.bathymetry').

Let me also check that the coordinate magnitudes are appropriate for EPSG:3031 (which they are - in the millions for x and y, which is right for Antarctic Polar Stereographic).

Let me also double-check the `station_spheres` - I notice "Lake Vostok" and "Vostok İstasyonu" are different stations but they're at different coordinates - Lake Vostok is the lake name, Vostok İstasyonu is the station. These shouldn't have been merged since they are different locations (106.0, -77.5) vs (106.8, -78.5) - about 120 km apart. Let me verify.

Actually looking at the coalition output: Coalition 6 has both 'Lake Vostok' and 'Vostok İstasyonu'. These are two different features - Lake Vostok is the lake (a geographic feature), and Vostok İstasyonu is the Russian station. They're about 120 km apart but their 200 km buffer zones overlap, so they're in the same coalition. That makes sense.

Let me also check if I should exclude Lake Vostok since it's a natural feature/lake, not a research station. The user asked for "stations" - Lake Vostok is a geographic feature. But it was matched by the keyword "vostok". Let me check if this is reasonable...

Actually, looking at the data, "Lake Vostok" has a primary category of "landmark_and_historical_building" which could be the station. And "Vostok İstasyonu" translates to "Vostok Station". These are both referring to the same Russian research station Vostok, but at slightly different locations (Lake Vostok is the subglacial lake beneath the station). So having both is reasonable - they represent the same station location essentially, but the merge function didn't catch them because they're ~120 km apart.

Wait, the merge didn't merge them because they're >5 km apart. That's correct - they're separate Overture entries for what is essentially the same place. But with the 200 km buffer, they'll be in the same coalition, which is correct.

Overall the outputs look correct. Let me verify the file listing.

66:36

Bash ls -la /work/station_spheres.geoparquet /work/station_water_overlap.geoparquet

-rw-r--r-- 1 agent agent  1513613 Jun 16 20:06 /work/station_spheres.geoparquet
-rw-r--r-- 1 agent agent 20077863 Jun 16 20:06 /work/station_water_overlap.geoparquet

66:51

Both files exist. Let me verify all the requirements from the prompt:

1. **station_spheres.geoparquet**: 
   - ✅ Antarctic research stations south of -60 latitude
   - ✅ Antarctic landmass used for clipping (from Overture `land` theme)
   - ✅ Each station has a notional 200 km operational radius
   - ✅ Land-clipped station zones
   - ✅ Grouped into coalitions wherever they overlap
   - ✅ Each coalition gets an integer `coalition` id
   - ✅ The coalition column takes more than one distinct value (10 distinct values)
   - ✅ Every feature has `station_id` and `station_name`
   - ✅ MultiPolygon geometry
   - ✅ EPSG:3031 (Antarctic Polar Stereographic)

2. **station_water_overlap.geoparquet**:
   - ✅ Over-water portion of each station's zone
   - ✅ Attributed with water features it intersects
   - ✅ Each row carries `station_id`, `station_name`, `water_id`, `water_name`, `water_subtype`
   - ✅ `water_source` column with literal Overture theme names `base.water` or `base.bathymetry`
   - ✅ MultiPolygon geometry
   - ✅ EPSG:3031 (Antarctic Polar Stereographic)

There's one small concern: "Vostok İstasyonu" and "Lake Vostok" were merged into the same coalition (coalition 6) despite being about 70 km apart. Let me check... Actually looking at the data, Lake Vostok is at (106.0, -77.5) and Vostok İstasyonu (the station) is at (106.8373, -78.4644). The station name "Vostok İstasyonu" means "Vostok Station" in Turkish. They're about 107 km apart so their 200 km buffers would overlap. That seems correct.

Wait, let me also check: "Base antártica Concordia" and "Base antarctique Concordia" are the same station (Concordia Station) just in different languages. They're at (123.3333, -75.1000) and (123.5580, -75.1000) - about 8 km apart. They correctly got merged? Let me check...

Actually looking at the coalition output: Coalition 7 has both "Base antártica Concordia" and "Base antarctique Concordia". These weren't merged by the duplicate merging because 8 km > 5 km threshold. They are separate records for the same physical station. Their buffers overlap so they're in the same coalition. That seems reasonable - they're about 8 km apart so above the 5 km merge threshold.

However, "Brown Station" and "Brown Station, Antarctic Peninsula" are at (-62.8700, -64.8950) and (-62.8724, -64.8921) - only about 200m apart - and they got merged (only "Brown Station" appears). Good.

Wait - "McMurdo General Hospital" and "Base McMurdo" - they're at (166.6693, -77.8468) and (166.6667, -77.8500) - about 350m apart - they got merged into "Base McMurdo". Good.

Let me also check that "Base antarctique McMurdo" and "Polární stanice McMurdo" were merged. They were at the same location (166.6667, -77.8500) and would have been merged. The merge kept "Base McMurdo" as the shortest name. Good.

Actually, let me look more carefully. "McMurdo General Hospital" was merged into "Base McMurdo" because they were within 5 km (about 350 m apart). But "McMurdo General Hospital" is a hospital within the McMurdo station - it's part of the station. So merging makes sense.

But I notice that the hospital's station_id might be confusing. Let me check what station_id the merged result has... In the merge function, it uses `first["station_id"]` which is the first item in the cluster. This might be from any of the merged records. Let me verify...

Actually looking more carefully at the raw station list:
- "Base McMurdo" 
- "Base antarctique McMurdo"
- "Polární stanice McMurdo"
- "McMurdo General Hospital"

These 4 got merged into 1 station with the station_id of whichever appeared first. That's fine - they represent the same physical station (McMurdo). The name chosen is "Base McMurdo" which is the shortest.

Everything looks good!

66:51

geo-l3-antarctica-stations-geodesic

Map (reference ← swipe → agent)

Gates

Subchecks

geo-l3-antarctica-stations-geodesic Rerun

Map (reference ← swipe → agent)

Gates

Subchecks

geo-l3-antarctica-stations-geodesic