geo-l3-antarctica-stations-geodesic

Rerun

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

Help on module overturemaps.core in overturemaps:

NAME
    overturemaps.core

FUNCTIONS
    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.

    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

    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

    get_all_overture_types() -> List[str]

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

        Returns
        -------
        Tuple of (all_releases, latest_release) where:

address
bathymetry
building
building_part
connector
division
division_area
division_boundary
infrastructure
land
land_cover
land_use
place
segment
water

python -c "
import overturemaps
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2025-02-19-alpha')
print(list(df.columns))
print(df.dtypes)
print(len(df))
print(df.locale.unique() if hasattr(df, 'locale') else 'no locale')
" 2>&1 | tail -30

Traceback (most recent call last):
  File "<string>", line 3, in <module>
    df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2025-02-19-alpha')
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 342, in geodataframe
    reader = record_batch_reader(
        overture_type,
    ...<4 lines>...
        stac=stac,
    )
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 307, in record_batch_reader
    result = _prepare_query(
        overture_type, bbox, release, connect_timeout, request_timeout, stac
    )
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 266, in _prepare_query
    dataset = ds.dataset(
        intersecting_files if intersecting_files is not None else path,
    ...<5 lines>...
        ),
    )
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 790, in dataset
    return _filesystem_dataset(source, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 472, in _filesystem_dataset
    fs, paths_or_selector = _ensure_single_source(source, filesystem)
                            ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 437, in _ensure_single_source
    raise FileNotFoundError(path)
FileNotFoundError: overturemaps-us-west-2/release/2025-02-19-alpha/theme=places/type=place/

python -c "
import overturemaps
releases = overturemaps.get_available_releases()
print('All releases:', releases[0])
print('Latest:', releases[1])
" 2>&1

Traceback (most recent call last):
  File "<string>", line 3, in <module>
    releases = overturemaps.get_available_releases()
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: module 'overturemaps' has no attribute 'get_available_releases'

python -c "
from overturemaps.core import get_available_releases
releases = get_available_releases()
print('All:', releases[0])
print('Latest:', releases[1])
" 2>&1

python -c "
import overturemaps
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
print(list(df.columns))
print(df.dtypes)
print(len(df))
print(df[df.geometry.notna()].head(20)[['name','country','categories','subtype','locales']])
" 2>&1 | tail -40

['id', 'geometry', 'categories', 'confidence', 'websites', 'emails', 'socials', 'phones', 'brand', 'addresses', 'names', 'sources', 'operating_status', 'basic_category', 'taxonomy', 'version', 'bbox']
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
2348
Traceback (most recent call last):
  File "<string>", line 7, in <module>
    print(df[df.geometry.notna()].head(20)[['name','country','categories','subtype','locales']])
          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 1891, in __getitem__
    result = super().__getitem__(key)
  File "/usr/local/lib/python3.14/site-packages/pandas/core/frame.py", line 4384, in __getitem__
    indexer = self.columns._get_indexer_strict(key, "columns")[1]
              ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 6302, in _get_indexer_strict
    self._raise_if_missing(keyarr, indexer, axis_name)
    ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 6355, in _raise_if_missing
    raise KeyError(f"{not_found} not in index")
KeyError: "['name', 'country', 'subtype', 'locales'] not in index"

python -c "
import overturemaps
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
print('Columns:', list(df.columns))
print()
print('Sample names:', df['names'].head(5))
print()
print('Sample categories:', df['categories'].head(5))
print()
print('Sample basic_category:', df['basic_category'].head(10))
print()
# Check for research stations - look at names for 'station' or 'base'
names_col = df['names'].explode().dropna().unique()
station_names = [n for n in names_col if isinstance(n, str) and ('station' in n.lower() or 'base' in n.lower() or 'research' in n.lower())]
print('Station-related names:', station_names[:30])
" 2>&1 | tail -40

Columns: ['id', 'geometry', 'categories', 'confidence', 'websites', 'emails', 'socials', 'phones', 'brand', 'addresses', 'names', 'sources', 'operating_status', 'basic_category', 'taxonomy', 'version', 'bbox']

Sample names: 0    {'primary': 'Tincorp Pa TI', 'common': None, '...
1    {'primary': 'Sara Couto - Intervenção Terapêut...
2    {'primary': 'แหลมใหญ่แล็บ คลินิกเทคนิคการแพทย์...
3    {'primary': 'Chittagong Victory National Schoo...
4    {'primary': 'Quảng Cáo - In Nhanh SaPa', 'comm...
Name: names, dtype: object

Sample categories: 0    {'primary': 'information_technology_company', ...
1    {'primary': 'physical_therapy', 'alternate': [...
2    {'primary': 'health_and_medical', 'alternate':...
3             {'primary': 'school', 'alternate': None}
4                                                 None
Name: categories, dtype: object

Sample basic_category: 0            corporate_or_business_office
1    physical_medicine_and_rehabilitation
2                                     NaN
3                       place_of_learning
4                                     NaN
5                                    farm
6                                     NaN
7                    sporting_goods_store
8                   animal_or_pet_service
9                    professional_service
Name: basic_category, dtype: str

Station-related names: []

python -c "
import overturemaps
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
# Check unique basic_categories
cats = df['basic_category'].dropna().unique()
print('Basic categories:', sorted(cats))
print()
# Check taxonomy
taxs = df['taxonomy'].dropna().unique()
print('Sample taxonomies:', taxs[:20])
" 2>&1 | tail -40

Basic categories: ['agricultural_service', 'airport', 'animal_and_pet_store', 'animal_or_pet_service', 'art_gallery', 'arts_crafts_and_hobby_store', 'astrological_advising', 'attorney_or_law_firm', 'auto_dealer', 'automotive_service', 'b2b_energy_and_utility_service', 'b2b_industrial_and_machine_service', 'b2b_office_and_professional_service', 'b2b_science_and_technology_service', 'b2b_service', 'b2b_transportation_and_storage_service', 'bank_or_credit_union', 'bar', 'beach', 'behavioral_or_mental_health_clinic', 'books_music_and_video_store', 'buddhist_place_of_worship', 'building_or_construction_service', 'cafe', 'campground', 'campus_building', 'casual_eatery', 'christian_place_of_worship', 'civic_organization', 'coffee_shop', 'college_university', 'community_center', 'complementary_and_alternative_medicine', 'convenience_store', 'corporate_or_business_office', 'cultural_center', 'dental_clinic', 'design_service', 'diagnostics_imaging_or_lab_service', 'educational_facility', 'educational_service', 'electronics_store', 'elementary_school', 'embassy', 'emergency_department', 'emergency_or_urgent_care_facility', 'event_or_party_service', 'event_venue', 'family_service', 'farm', 'fashion_and_apparel_store', 'fast_food_restaurant', 'financial_service', 'fitness_studio', 'flowers_and_gifts_store', 'food_and_beverage_store', 'food_service', 'food_truck_stand', 'gaming_venue', 'gas_station', 'government_office', 'ground_transport_facility_or_service', 'gym', 'hardware_home_and_garden_store', 'high_school', 'hindu_place_of_worship', 'historic_site', 'home_service', 'hospital', 'hotel', 'industrial_facility_or_service', 'lake', 'laundry_service', 'legal_service', 'library', 'lodging', 'lounge', 'manufacturer', 'media_service', 'medical_service', 'middle_school', 'monument', 'mountain', 'museum', 'music_venue', 'musical_instrument_and_pro_audio_store', 'muslim_place_of_worship', 'national_park', 'non_alcoholic_beverage_venue', 'office_supply_store', 'outpatient_care_facility', 'park', 'personal_care_and_beauty_store', 'personal_or_beauty_service', 'pharmacy_and_drug_store', 'physical_medicine_and_rehabilitation', 'place_of_learning', 'political_organization', 'preschool', 'primary_care_or_general_clinic', 'print_media_service', 'printing_service', 'private_lodging', 'professional_service', 'psychic_advising', 'public_fountain', 'public_utility', 'radio_station', 'real_estate_service', 'recreational_equipment_rental', 'religious_organization', 'rental_service', 'reproductive_perinatal_and_womens_care', 'research_institute', 'resort', 'restaurant', 'river', 'rv_park', 'second_hand_store', 'senior_living_facility', 'shipping_or_delivery_service', 'shopping_mall', 'smoothie_juice_bar', 'social_or_community_service', 'specialized_health_care', 'specialized_medical_facility', 'specialty_school', 'specialty_store', 'sport_field', 'sport_or_fitness_facility', 'sport_or_recreation_club', 'sport_team', 'sporting_goods_store', 'stadium_arena', 'storage_facility', 'superstore', 'supplier_or_distributor', 'surgery', 'technical_service', 'theatre_venue', 'toys_and_games_store', 'train_station', 'travel_service', 'tutoring_service', 'vehicle_dealer', 'vehicle_parts_store', 'vehicle_service', 'vision_or_eye_care_clinic', 'walk_in_clinic', 'warehouse_club_store', 'wellness_service', 'winery']

Traceback (most recent call last):
  File "<string>", line 9, in <module>
    taxs = df['taxonomy'].dropna().unique()
  File "/usr/local/lib/python3.14/site-packages/pandas/core/series.py", line 2228, in unique
    return super().unique()
           ~~~~~~~~~~~~~~^^
  File "/usr/local/lib/python3.14/site-packages/pandas/core/base.py", line 1159, in unique
    result = algorithms.unique1d(values)  # type: ignore[assignment]
  File "/usr/local/lib/python3.14/site-packages/pandas/core/algorithms.py", line 433, in unique
    return unique_with_mask(values)
  File "/usr/local/lib/python3.14/site-packages/pandas/core/algorithms.py", line 476, in unique_with_mask
    uniques = table.unique(values)
  File "pandas/_libs/hashtable_class_helper.pxi", line 7840, in pandas._libs.hashtable.PyObjectHashTable.unique
  File "pandas/_libs/hashtable_class_helper.pxi", line 7783, in pandas._libs.hashtable.PyObjectHashTable._unique
TypeError: unhashable type: 'dict'

python -c "
import overturemaps
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
# Look at primary names
primaries = df['names'].apply(lambda x: x.get('primary') if isinstance(x, dict) else None)
primaries = primaries.dropna()
print('Total places:', len(primaries))
# Filter for things that look like research stations
station_keywords = ['station', 'base', 'research', 'observatory', 'camp', 'shelter', 'hut']
for kw in station_keywords:
    matches = primaries[primaries.str.lower().str.contains(kw, na=False)]
    if len(matches) > 0:
        print(f'\n--- Matches for \"{kw}\": {len(matches)} ---')
        for n in matches.head(20):
            print(f'  {n}')
" 2>&1 | tail -80

Total places: 2348

--- Matches for "station": 13 ---
  Master Bees Stationeries & Souvenirs
  Brown Station, Antarctic Peninsula
  Brown Station
  Palmer Station, Antarctica
  New Lucena Municipal Police Station
  Bellingshausen Russian Antarctic Station
  Neumayer-Station II
  Fuel Tech Micro Gas Station & Dispensers
  Plateau Station
  Pioneer - Gas Station
  Antarctic Kunlun Station
  COFFE Station Old town
  Mario-Zucchelli-Station

--- Matches for "base": 13 ---
  González Videla Antarctic Base
  Carlini Base
  Base Marambio, Antartida Argentina
  Marambio Base
  Base Antártica Marambio
  Esperanza Base, Antarctica
  Base Esperanza, Antartida Argentina
  Base antarctique Mawson
  Base antártica Concordia
  Base antarctique Concordia
  Base antarctique Dumont-d'Urville
  Base McMurdo
  Base antarctique McMurdo

--- Matches for "research": 1 ---
  Lakeview Clinical Research

--- Matches for "camp": 10 ---
  Dhacss Seaview Campus
  Noakhali Ideal Polytechnic Institute - Digital Campus
  Camp Education Society's Dr. Arvind B. Telang IHM Chinchwad Pune - 19
  Cidahu Camping Ground
  Campo Creek Vineyards
  The Mahanaim - God's Camp
  Vintage Cocktail Camper
  VU GIFT Campus Jhang
  Escola Técnica de Teatro Professor José Gomes Campos
  JRMSU Katipunan Campus CTEd-Laboratory High School

--- Matches for "hut": 1 ---
  Rashid Rafique Bhutta

python -c "
import overturemaps
import geopandas as gpd

df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')

# Antarctic station keywords
ant_kw = ['station', 'base', 'antarct']
mask = df['names'].apply(
    lambda x: any(kw in str(x).lower() for kw in ant_kw) if isinstance(x, dict) else False
)
stations = df[mask].copy()
print(f'Potential stations: {len(stations)}')
for idx, row in stations.iterrows():
    nm = row['names']
    geo = row['geometry']
    print(f'  {nm.get(\"primary\",\"?\")} | lat={geo.y:.3f} lon={geo.x:.3f} | id={row[\"id\"][:20]}')
" 2>&1 | tail -40

  Port Lockroy, Antarctic Peninsula | lat=-64.825 lon=-63.491 | id=2a100ade-be10-41c0-b
  Mount William (Antarctica) | lat=-64.783 lon=-63.683 | id=95a94038-ab7f-456c-a
  Lemaire Channel, Antarctic Peninsula | lat=-65.103 lon=-63.982 | id=7da59934-acaf-4bea-8
  Palmer Station, Antarctica | lat=-64.774 lon=-64.053 | id=f73848f3-abaa-432b-b
  Mount Jackson (Antarctica) | lat=-71.364 lon=-63.412 | id=d65cdcdb-703f-48cb-a
  New Lucena Municipal Police Station | lat=-80.872 lon=-61.348 | id=92195dea-f395-4151-a
  Esperanto Island, Antarctica | lat=-62.641 lon=-60.365 | id=3bf8e968-6050-4ee6-8
  Cierva Cove, Antarctic Peninsula | lat=-64.154 lon=-60.905 | id=5c3dfca0-16e9-4630-9
  Antarctica, Antarctic Circle | lat=-64.054 lon=-60.932 | id=9d364bf8-eaaf-4f79-a
  Mikkelsen Harbour, Antarctic Peninsula | lat=-63.896 lon=-60.765 | id=e254628a-91d9-4e80-b
  Carlini Base | lat=-62.238 lon=-58.667 | id=c37b7fbf-894a-4c23-9
  Bellingshausen Russian Antarctic Station | lat=-62.200 lon=-58.962 | id=6240b821-30cb-4fe4-a
  King George Bay (Antarctica) | lat=-62.100 lon=-58.083 | id=78c69e17-0ff6-4498-b
  Base Marambio, Antartida Argentina | lat=-64.241 lon=-56.620 | id=18414d58-4e4c-480f-b
  Marambio Base | lat=-64.241 lon=-56.627 | id=015df87e-1265-4d80-8
  Base Antártica Marambio | lat=-64.237 lon=-56.624 | id=11037d14-337b-4a53-a
  Chapel of the Blessed Virgin of Lujan, Antarctica | lat=-64.241 lon=-56.628 | id=4775fc70-d68b-48b4-a
  Esperanza Base, Antarctica | lat=-63.398 lon=-56.996 | id=8536af5a-82ba-46ed-b
  Base Esperanza, Antartida Argentina | lat=-63.398 lon=-56.997 | id=b1662153-d852-4a90-b
  British Antarctic Territory | lat=-75.000 lon=-50.000 | id=0f82cde8-1ca3-41ca-9
  Neumayer-Station II | lat=-70.650 lon=-8.250 | id=0038d9b1-d883-46c9-b
  Antarctica/Troll | lat=-72.011 lon=2.535 | id=e3417e97-f283-4ecc-a
  Fuel Tech Micro Gas Station & Dispensers | lat=-65.339 lon=15.469 | id=4f2fab09-8808-425a-a
  Plateau Station | lat=-79.250 lon=40.500 | id=91c5d93e-1600-41e7-8
  Pioneer - Gas Station | lat=-78.910 lon=43.926 | id=8bdf0626-6096-472f-b
  Lake Glubokoye (Antarctica) | lat=-67.667 lon=45.867 | id=37ec72ed-aef2-436d-b
  Beaver Lake (Antarctica) | lat=-70.783 lon=68.300 | id=234aa592-5b23-41d4-8
  Nicholas Range (Antarctic) | lat=-66.667 lon=55.617 | id=bf4691e5-cd16-4a40-8
  Base antarctique Mawson | lat=-67.603 lon=62.874 | id=0f436ec9-9483-4218-8
  East Antarctica | lat=-80.000 lon=80.000 | id=f5e5d673-82d4-4316-a
  Antarctic Kunlun Station | lat=-80.417 lon=77.116 | id=91c1becd-082b-4bda-8
  COFFE Station Old town | lat=-84.608 lon=116.719 | id=0ac11701-df43-4927-8
  Base antártica Concordia | lat=-75.100 lon=123.333 | id=c5fd752b-15d7-46ec-a
  Base antarctique Concordia | lat=-75.100 lon=123.558 | id=c0db4819-2dc3-4e47-8
  Base antarctique Dumont-d'Urville | lat=-66.663 lon=140.001 | id=04d8258a-e765-4a4d-9
  Antarctic Plateau | lat=-77.000 lon=150.000 | id=26f0564a-e108-46da-b
  Lake Bonney (Antarctica) | lat=-77.717 lon=162.417 | id=40060107-a899-46b3-8
  Mario-Zucchelli-Station | lat=-74.694 lon=164.112 | id=fcdd73f9-5cb8-4812-b
  Base McMurdo | lat=-77.850 lon=166.667 | id=d5882de0-07ff-46b9-a
  Base antarctique McMurdo | lat=-77.850 lon=166.667 | id=d74e5a73-042c-4694-9

python -c "
import overturemaps
# Check infrastructure type
df2 = overturemaps.geodataframe('infrastructure', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
print('Infrastructure columns:', list(df2.columns))
print('Length:', len(df2))
print('Subtype:', df2['subtype'].dropna().unique() if 'subtype' in df2.columns else 'N/A')
# Check names
if 'names' in df2.columns:
    primaries = df2['names'].apply(lambda x: x.get('primary') if isinstance(x, dict) else None)
    primaries = primaries.dropna()
    for n in primaries.head(30):
        print(f'  {n}')
" 2>&1 | tail -40

Infrastructure columns: ['id', 'geometry', 'sources', 'names', 'level', 'wikidata', 'source_tags', 'subtype', 'class', 'height', 'surface', 'version', 'bbox']
Length: 944
Subtype: <ArrowStringArray>
[      'pedestrian',            'tower',          'airport',
   'transportation',       'recreation',             'quay',
             'pier',          'utility',    'communication',
          'barrier',            'power',            'water',
          'transit',           'bridge', 'waste_management',
        'aerialway']
Length: 16, dtype: str
  1306 km to South Pole
  Marlene Automatic Weather Station
  657 km to South Pole
  Thiel Mountains - Skiway
  Union Glacier Airport
  Union Glacier Station
  Patriot Hills Airport
  Sky Blu Airfield
  Rothera Research Station Airfield
  Rothera Skiway
  Fossil Bluff Skiway
  San Martín Base
  Старий паливний бак
  Паливний бак
  Eisbergfriedhof
  Palmer Station Skiway
  Tank 2
  Tank 1
  Golf Ball
  Satellite Radome
  Paradise Bay
  Dorian Bay Viewpoint
  Skua Nests Viewpoint
  British Refuge Hut
  Neptune's Window
  Estación PEVIMA
  Estación Antártica PEVIMA
  Matienzo Base
  Aeródromo Teniente Rodolfo Marsh Martin
  Transmisora

python -c "
import overturemaps
# Check division_area
df = overturemaps.geodataframe('division_area', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
print('division_area cols:', list(df.columns))
print('Len:', len(df))
# Check division_boundary
df2 = overturemaps.geodataframe('division_boundary', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
print('division_boundary cols:', list(df2.columns))
print('Len:', len(df2))
" 2>&1 | tail -20

division_area cols: ['id', 'geometry', 'country', 'sources', 'subtype', 'admin_level', 'class', 'names', 'is_land', 'is_territorial', 'region', 'division_id', 'version', 'bbox']
Len: 3
division_boundary cols: ['id', 'geometry', 'division_ids', 'subtype', 'admin_level', 'class', 'sources', 'perspectives', 'is_disputed', 'is_land', 'is_territorial', 'country', 'region', 'version', 'bbox']
Len: 0

python -c "
import overturemaps
# Look at infrastructure more closely for stations
df = overturemaps.geodataframe('infrastructure', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
# Get names with 'station' or 'base'
for idx, row in df.iterrows():
    nm = row.get('names', {})
    if isinstance(nm, dict):
        p = nm.get('primary', '')
        if any(kw in p.lower() for kw in ['station', 'base', 'research']):
            print(f'{p} | subtype={row[\"subtype\"]} | class={row[\"class\"]} | geom_type={row.geometry.geom_type}')
" 2>&1 | tail -40

Marlene Automatic Weather Station | subtype=tower | class=observation | geom_type=Point
Union Glacier Station | subtype=recreation | class=camp_site | geom_type=Point
Rothera Research Station Airfield | subtype=airport | class=airport | geom_type=Point
San Martín Base | subtype=airport | class=airport | geom_type=Point
Palmer Station Skiway | subtype=airport | class=airport | geom_type=Point
Matienzo Base | subtype=airport | class=airport | geom_type=Point
Puerto Moro (Base Esperanza) | subtype=transit | class=ferry_terminal | geom_type=Point
Base Antártica Orcadas | subtype=airport | class=airport | geom_type=Point
Halley Station Skiway | subtype=airport | class=airport | geom_type=Point
Skipiste Neumayer-Station | subtype=airport | class=airport | geom_type=Point
Kohnen Station Skiway | subtype=airport | class=airport | geom_type=Point
Troll main powerstation | subtype=power | class=generator | geom_type=Polygon
Troll backup power station | subtype=power | class=generator | geom_type=Polygon
Showa station Skiway | subtype=airport | class=airport | geom_type=Point
Plateau Station Skiway | subtype=airport | class=airport | geom_type=Point
Molodezhnaya Station Airfield | subtype=airport | class=airport | geom_type=Point
Mawson Station Skiway | subtype=airport | class=airport | geom_type=Point
Bharati Indian Antartic Station | subtype=transit | class=ferry_terminal | geom_type=Point
Progress Station | subtype=transit | class=ferry_terminal | geom_type=Point
Vostok Station Skiway | subtype=airport | class=airport | geom_type=Polygon
Vostok Station | subtype=communication | class=communication_tower | geom_type=Point
Casey Station Skiway | subtype=airport | class=airport | geom_type=Point
Station Concordia | subtype=airport | class=airport | geom_type=Polygon
Aeroporto Mario Zucchelli Station | subtype=airport | class=airport | geom_type=Point
Zucchelli station aerodrome | subtype=airport | class=airport | geom_type=Point
McMurdo Station - Scott Base | subtype=pedestrian | class=information | geom_type=Point

python -c "
import overturemaps
# Get place and look more carefully at categories
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')

# Check categories more carefully
cats = df['categories'].dropna()
print('Sample categories:', cats.head(10))
print()
# Check for research_institute in categories
for idx, row in df.iterrows():
    cat = row['categories']
    if isinstance(cat, dict):
        prim = cat.get('primary', '')
        if 'research' in prim.lower() or 'station' in prim.lower():
            nm = row['names'].get('primary', '?') if isinstance(row['names'], dict) else '?'
            print(f'{nm} | category={prim} | lat={row.geometry.y:.3f} lon={row.geometry.x:.3f}')
" 2>&1 | tail -40

Sample categories: 0     {'primary': 'information_technology_company', ...
1     {'primary': 'physical_therapy', 'alternate': [...
2     {'primary': 'health_and_medical', 'alternate':...
3              {'primary': 'school', 'alternate': None}
5                {'primary': 'farm', 'alternate': None}
7         {'primary': 'sports_wear', 'alternate': None}
8         {'primary': 'pet_groomer', 'alternate': None}
9     {'primary': 'professional_services', 'alternat...
10        {'primary': 'real_estate', 'alternate': None}
11    {'primary': 'landmark_and_historical_building'...
Name: categories, dtype: object

Bellingshausen Russian Antarctic Station | category=educational_research_institute | lat=-62.200 lon=-58.962
Fuzion Radio Medellin | category=radio_station | lat=-78.144 lon=-45.191
Carrefour Posto João Pessoa | category=gas_station | lat=-70.857 lon=-34.847
Carrefour Posto Recife | category=gas_station | lat=-80.408 lon=-34.910
Pioneer | category=gas_station | lat=-78.910 lon=43.926
Pioneer - Gas Station | category=gas_station | lat=-78.910 lon=43.926
Base antártica Concordia | category=train_station | lat=-75.100 lon=123.333
Rádio Alerta Penedo | category=radio_station | lat=-68.206 lon=127.829
Seaoil Dangcagan | category=gas_station | lat=-80.457 lon=135.780
Lakeview Clinical Research | category=medical_research_and_development | lat=-64.414 lon=175.781

python -c "
import overturemaps
# Get ALL place features south of -60
import pandas as pd
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')

# This was filtered by bbox so all are south of -60
# Check what features have research institute category
research = df[df['categories'].apply(lambda x: isinstance(x, dict) and x.get('primary') == 'educational_research_institute')]
print(f'Research institutes: {len(research)}')
for idx, row in research.iterrows():
    nm = row['names'].get('primary','?') if isinstance(row['names'], dict) else '?'
    print(f'  {nm} | {row.geometry.y:.3f}, {row.geometry.x:.3f}')

# Also check historical and landmark 
hmm = df[df['categories'].apply(lambda x: isinstance(x, dict) and ('historical' in str(x.get('primary','')).lower() or 'landmark' in str(x.get('primary','')).lower()))]
print(f'\nHistorical/landmark: {len(hmm)}')
for idx, row in hmm.iterrows():
    nm = row['names'].get('primary','?') if isinstance(row['names'], dict) else '?'
    print(f'  {nm} | {row.geometry.y:.3f}, {row.geometry.x:.3f}')
" 2>&1

Research institutes: 1
  Bellingshausen Russian Antarctic Station | -62.200, -58.962

Historical/landmark: 163
  Ross Ice Shelf, Antarctica | -70.613, -177.891
  Moody Island | -77.333, -149.200
  Vip House Apartment Pattaya | -77.157, -127.266
  Evans Point | -72.467, -99.383
  Urban Point | -79.800, -82.000
  Isla Charcot | -69.750, -75.250
  Stanton Hills | -75.283, -73.200
  智利南极领地 | -75.000, -71.500
  Patuxent Range | -84.717, -64.500
  Antarctic Peninsula | -69.500, -65.000
  Mount Kennett | -67.050, -65.167
  Penny Ice Cap | -67.250, -66.000
  Fishtrap Cove | -68.183, -67.000
  Low Island (South Shetland Islands) | -63.283, -62.167
  Petroff Point | -64.187, -62.091
  Lewis Point | -69.900, -62.417
  Gerlache Strait | -64.500, -62.333
  Cuverville Island | -64.683, -62.633
  Errera Channel, Antarctica | -64.696, -62.585
  Paradise Bay, Antarctic Peninsula | -64.908, -62.904
  Brown Station, Antarctic Peninsula | -64.892, -62.872
  Brown Station | -64.895, -62.870
  Haigh Point | -64.917, -63.100
  Port Lockroy, Antarctic Peninsula | -64.825, -63.491
  Lemaire Channel | -65.093, -63.968
  Lemaire Channel, Antarctic Peninsula | -65.103, -63.982
  Knight Island | -64.917, -64.017
  Elephant Rocks | -64.767, -64.083
  Gossler Islands | -64.700, -64.367
  King Ridge | -84.633, -64.083
  Esperanto Island, Antarctica | -62.641, -60.365
  Cierva Cove, Antarctic Peninsula | -64.154, -60.905
  Antarctica, Antarctic Circle | -64.054, -60.932
  Mikkelsen Harbour, Antarctic Peninsula | -63.896, -60.765
  Trinity Island | -63.783, -60.733
  Nelson Island (South Shetland Islands) | -62.300, -59.050
  中国南极长城站 | -62.216, -58.964
  Rip Point | -62.250, -58.983
  Labbé Point | -62.497, -59.731
  Yankee Harbor | -62.533, -59.782
  Kendall Rocks | -63.500, -59.817
  Jurien Island | -63.533, -59.817
  King George Bay (Antarctica) | -62.100, -58.083
  Ilha do Rei George | -62.033, -58.350
  Gam Point | -61.923, -57.955
  Lermanda | -63.317, -57.917
  Jones Valley | -83.917, -56.833
  Mount Moffat | -83.533, -55.283
  Esperanza Base, Antarctica | -63.398, -56.996
  British Antarctic Territory | -75.000, -50.000
  Tsuru Villa | -70.081, -39.105
  Foul Point | -60.533, -45.483
  Inner Sanctum | -60.673, -45.626
  Amos Lake | -60.700, -45.650
  Foca Point | -60.700, -45.667
  Lichtner Seamount | -67.550, -0.667
  Queen Maud Land | -73.050, -13.417
  Kuvsletta Flat | -73.833, -5.233
  Framranten Point | -73.817, -5.217
  Neumayer-Station II | -70.650, -8.250
  Iceport | -70.583, -7.850
  Knerten Rock | -71.550, -2.867
  Krylvika Bight | -71.333, -2.000
  Jutulgryta Crevasses | -71.267, 0.450
  Kvitskarvhalsen Saddle | -72.500, 0.850
  Kvitsvodene Valley | -72.433, 0.750
  Knattebrauta Nunataks | -72.450, 0.300
  Antarctica/Troll | -72.011, 2.535
  Lars Nunatak | -71.867, 4.217
  Mount Ruhnke | -72.083, 3.633
  SANAE | -72.667, 2.850
  Kamenev Bight | -69.917, 9.500
  Kruber Rock | -71.750, 11.083
  Mount Flånuten | -71.783, 11.283
  Kvitkleven Cirque | -72.000, 7.717
  Kubusdaelda | -71.983, 7.433
  Kubusdalen | -71.967, 7.233
  Larsen Cliffs | -71.933, 6.883
  Kyrkjedalen Valley | -71.833, 6.883
  Kyrkjebakken Slope | -71.900, 6.533
  Katedralen Canyon | -71.867, 6.550
  Helle Slope | -71.417, 5.250
  Kvamsgavlen Cliff | -71.767, 11.833
  Southern Ocean | -80.761, 25.312
  Parkview Apartments | -77.313, 35.859
  For Sale condo in Qc Ready for Occupancy | -78.904, 36.562
  Plateau Station | -79.250, 40.500
  Lake Lagernoye | -67.667, 45.850
  Cape Granat | -67.650, 45.850
  Lamykin Dome | -67.450, 46.667
  Felton Head | -67.283, 46.983
  Mount Hollingsworth | -67.250, 50.367
  Mount Letten | -66.917, 51.050
  Kyle Nunataks | -66.783, 51.333
  Mount Keyser | -66.933, 52.383
  Vicars Island | -65.850, 54.400
  Sakellari Peninsula | -67.067, 49.033
  昭和基地 | -69.006, 39.590
  Karamete Point | -69.150, 35.433
  Filla Island | -68.817, 77.833
  Krok Fjord | -68.667, 78.000
  Ellis Fjord | -68.600, 78.083
  Heidemann Bay | -68.583, 77.967
  Lied Bluff | -68.517, 78.267
  Langnes Fjord | -68.500, 78.250
  Lake Zvezda | -68.533, 78.450
  Lake Jabs | -68.550, 78.250
  Ingrid Christensen Coast | -69.500, 77.000
  Radok Lake | -70.867, 68.000
  Beaver Lake (Antarctica) | -70.783, 68.300
  Jetty Peninsula | -70.500, 68.900
  Lambert Graben | -70.000, 70.000
  Bjerkø Peninsula | -67.833, 69.500
  Lars-Christensen-Küste | -69.000, 69.000
  तराई ईट्टा सप्लायर्स | -66.174, 63.640
  Rippon Glacier | -66.667, 56.483
  Edward VIII Bay | -66.833, 57.000
  Kvarsnes Foreland | -67.033, 57.000
  Kloa Point | -66.633, 57.317
  Kemp Coast | -67.250, 58.000
  Wilma Glacier | -67.200, 56.000
  Schwartz Range | -67.133, 55.633
  Mount Menzies | -73.500, 61.833
  Nelson Rock | -67.383, 62.750
  Kellas Islands | -67.550, 62.767
  Rouse Islands | -67.580, 62.950
  Welch Island | -67.560, 62.928
  Base antarctique Mawson | -67.603, 62.874
  Entrance Shoal | -67.600, 62.867
  Landmark Point | -67.517, 63.933
  Mount Lacey | -70.183, 64.717
  Mac. Robertson Land | -70.000, 65.000
  Mount Lied | -70.500, 65.550
  Kilfoyle Nunataks | -70.717, 65.850
  Mount Kerr | -70.433, 65.633
  Cape Fletcher | -67.683, 65.583
  Murray Monolith | -67.783, 66.883
  Scullin monolith | -67.783, 66.700
  Ridge A | -81.500, 73.500
  East Antarctica | -80.000, 80.000
  Dome A | -80.367, 77.350
  Antarctic Kunlun Station | -80.417, 77.116
  Jones Ridge | -66.600, 99.417
  แต้ย่งฮวด อพาร์ทเมนท์ พัทยาใต้ | -84.927, 101.244
  Lake Vostok | -77.500, 106.000
  Pole of Cold | -78.467, 106.800
  Vostok İstasyonu | -78.464, 106.837
  Bailey Rocks | -66.283, 110.533
  Blakeney Point | -66.238, 110.578
  Base antarctique Concordia | -75.100, 123.558
  Base antarctique Dumont-d'Urville | -66.663, 140.001
  South Magnetic Pole | -64.280, 136.590
  Antarctic Plateau | -77.000, 150.000
  Mount Ellis | -79.867, 156.233
  Mount Koons | -72.717, 160.367
  Canada Glacier | -77.617, 162.983
  Cape Armitage | -77.888, 166.591
  Base McMurdo | -77.850, 166.667
  Polární stanice McMurdo | -77.850, 166.667
  Base antarctique McMurdo | -77.850, 166.667
  Monte Erebus | -77.533, 167.283
  King Glacier | -83.483, 170.300
  Narrow Neck | -73.100, 169.050

python -c "
import overturemaps
# Check buildings for station names
df = overturemaps.geodataframe('building', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
print('Building columns:', list(df.columns))
print('Len:', len(df))
if 'names' in df.columns:
    names = df['names'].dropna()
    for idx, row in df.iterrows():
        nm = row.get('names')
        if isinstance(nm, dict):
            p = nm.get('primary','')
            if any(kw in p.lower() for kw in ['station', 'base', 'research']) and row.geometry.y < -60:
                print(f'  {p} | {row.geometry.y:.3f}, {row.geometry.x:.3f}')
" 2>&1 | tail -50

Building 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']
Len: 1404
Traceback (most recent call last):
  File "<string>", line 13, in <module>
    if any(kw in p.lower() for kw in ['station', 'base', 'research']) and row.geometry.y < -60:
                                                                          ^^^^^^^^^^^^^^
AttributeError: 'Polygon' object has no attribute 'y'. Did you mean: 'xy'?

python -c "
import overturemaps
# Check land theme
df = overturemaps.geodataframe('land', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
print('Land columns:', list(df.columns))
print('Len:', len(df))
print('Subtype:', df['subtype'].dropna().unique() if 'subtype' in df.columns else 'N/A')
print('Class:', df['class'].dropna().unique() if 'class' in df.columns else 'N/A')
# Check geometry types
print('Geom types:', df.geometry.geom_type.unique())
# Look at a sample
print(df.head(3))
" 2>&1

Land columns: ['id', 'names', 'subtype', 'class', 'sources', 'source_tags', 'level', 'wikidata', 'surface', 'elevation', 'geometry', 'version', 'bbox']
Len: 93367
Subtype: <ArrowStringArray>
[    'rock',     'land', 'physical',  'glacier',   'crater',     'sand',
     'reef',  'wetland',    'shrub',    'grass']
Length: 10, dtype: str
Class: <ArrowStringArray>
[           'bare_rock',                 'land',                 'peak',
                'cliff',              'glacier',                 'rock',
               'island',                'islet',                'scree',
               'saddle',               'valley',       'mountain_range',
            'peninsula',              'volcano', 'volcanic_caldera_rim',
          'archipelago',        'cave_entrance',                'ridge',
                'beach',                 'hill',                 'reef',
              'plateau',              'wetland',                'heath',
                'stone',               'tundra',                 'sand']
Length: 27, dtype: str
Geom types: <ArrowStringArray>
['Polygon', 'Point', 'MultiPolygon', 'LineString']
Length: 4, dtype: str
                                     id  ...                                               bbox
0  23f89603-d14d-3f1b-97c5-314269e17921  ...  {'xmin': -180.0, 'xmax': -179.97145080566406, ...
1  ef0ba39b-922b-3790-b71c-b5238e8f4e9b  ...  {'xmin': -179.9757080078125, 'xmax': -179.9248...
2  46a1ad5c-4af5-32f8-b0f5-c416ec492555  ...  {'xmin': -179.95298767089844, 'xmax': -179.859...

[3 rows x 13 columns]

python -c "
import overturemaps
# Check water theme
df = overturemaps.geodataframe('water', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
print('Water columns:', list(df.columns))
print('Len:', len(df))
print('Subtype:', df['subtype'].dropna().unique() if 'subtype' in df.columns else 'N/A')
print('Class:', df['class'].dropna().unique() if 'class' in df.columns else 'N/A')
print()
# Check bathymetry
df2 = overturemaps.geodataframe('bathymetry', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
print('Bathymetry columns:', list(df2.columns))
print('Len:', len(df2))
print('Subtype:', df2['subtype'].dropna().unique() if 'subtype' in df2.columns else 'N/A')
" 2>&1 | tail -30

Water columns: ['id', 'names', 'subtype', 'class', 'sources', 'source_tags', 'level', 'wikidata', 'is_intermittent', 'is_salt', 'geometry', 'version', 'bbox']
Len: 13501
Subtype: <ArrowStringArray>
[      'lake',   'physical',     'stream',      'ocean', 'human_made',
      'canal',       'pond',      'water',      'river',  'reservoir']
Length: 10, dtype: str
Class: <ArrowStringArray>
[         'lake',          'cape',        'stream',         'ocean',
           'bay',           'sea',        'strait', 'swimming_pool',
         'canal',          'pond',         'water',         'river',
         'ditch',        'lagoon', 'water_storage',         'shoal',
     'waterfall',       'fairway']
Length: 18, dtype: str

Bathymetry columns: ['id', 'depth', 'cartography', 'geometry', 'sources', 'version', 'bbox']
Len: 3096
Subtype: N/A

python -c "
import overturemaps

# Get all places and infrastructure
places = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
infra = overturemaps.geodataframe('infrastructure', bbox=(-180, -90, 180, -60), release='2026-05-20.0')

# Look at all unique categories in places
all_cats = set()
for c in places['categories'].dropna():
    if isinstance(c, dict):
        all_cats.add(c.get('primary',''))
print('All place categories (south of -60):')
for c in sorted(all_cats):
    print(f'  {c}')
" 2>&1 | tail -60

  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

python -c "
import overturemaps

# Get infrastructure features more carefully
infra = overturemaps.geodataframe('infrastructure', bbox=(-180, -90, 180, -60), release='2026-05-20.0')

# Get all names features that have 'station' in any field
for idx, row in infra.iterrows():
    nm = row.get('names')
    if isinstance(nm, dict):
        p = nm.get('primary', '')
        common = nm.get('common', '')
        if any(kw in str(p).lower() for kw in ['station', 'base']) or any(kw in str(common).lower() for kw in ['station', 'base']):
            print(f'{p} | subtype={row[\"subtype\"]} | class={row[\"class\"]} | {row.geometry.y:.3f}, {row.geometry.x:.3f}')
" 2>&1 | tail -60

Marlene Automatic Weather Station | subtype=tower | class=observation | -83.650, -167.400
Union Glacier Station | subtype=recreation | class=camp_site | -79.759, -82.827
Rothera Research Station Airfield | subtype=airport | class=airport | -67.568, -68.128
San Martín Base | subtype=airport | class=airport | -68.117, -67.100
Palmer Station Skiway | subtype=airport | class=airport | -64.775, -64.036
Matienzo Base | subtype=airport | class=airport | -64.000, -60.000
Puerto Moro (Base Esperanza) | subtype=transit | class=ferry_terminal | -63.395, -56.996
Base Antártica Orcadas | subtype=airport | class=airport | -60.738, -44.738
Halley Station Skiway | subtype=airport | class=airport | -75.582, -26.541
Skipiste Neumayer-Station | subtype=airport | class=airport | -70.646, -8.217
Kohnen Station Skiway | subtype=airport | class=airport | -75.001, 0.069
Traceback (most recent call last):
  File "<string>", line 14, in <module>
    print(f'{p} | subtype={row["subtype"]} | class={row["class"]} | {row.geometry.y:.3f}, {row.geometry.x:.3f}')
                                                                     ^^^^^^^^^^^^^^
AttributeError: 'Polygon' object has no attribute 'y'. Did you mean: 'xy'?

python -c "
import overturemaps

# Get ALL places and look thoroughly
places = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')

# Check categories that might be station-related
station_cats = ['educational_research_institute', 'research_institute', 'scientific_research']
for idx, row in places.iterrows():
    cat = row['categories']
    nm = row['names']
    if isinstance(cat, dict):
        p = cat.get('primary', '')
        if p in station_cats:
            name = nm.get('primary','?') if isinstance(nm, dict) else '?'
            print(f'{name} | {row.geometry.y:.3f}, {row.geometry.x:.3f} | cat={p}')
" 2>&1

python -c "
import overturemaps
# Also check land_use
df = overturemaps.geodataframe('land_use', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
print('Land use columns:', list(df.columns))
print('Len:', len(df))
" 2>&1

python -c "
import overturemaps

places = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')

# Get all place names with 'station' or 'base' or 'antarct' in the primary name
for idx, row in places.iterrows():
    nm = row['names']
    if isinstance(nm, dict):
        prim = nm.get('primary', '')
        if prim and any(kw in prim.lower() for kw in ['station', 'base', 'antarct']):
            cat = row['categories']
            cat_prim = cat.get('primary','?') if isinstance(cat, dict) else '?'
            lat, lon = row.geometry.y, row.geometry.x
            print(f'{prim:50s} | lat={lat:8.3f} lon={lon:8.3f} | cat={cat_prim}')
" 2>&1

Ross Ice Shelf, Antarctica                         | lat= -70.613 lon=-177.891 | cat=landmark_and_historical_building
West Antarctica                                    | lat= -79.000 lon=-100.000 | cat=national_park
Antarctic Peninsula                                | lat= -69.500 lon= -65.000 | cat=landmark_and_historical_building
Mount Wilson (Antarctica)                          | lat= -68.450 lon= -65.550 | cat=mountain
Master Bees Stationeries & Souvenirs               | lat= -75.141 lon= -66.797 | cat=souvenir_shop
Errera Channel, Antarctica                         | lat= -64.696 lon= -62.585 | cat=landmark_and_historical_building
Paradise Bay, Antarctic Peninsula                  | lat= -64.908 lon= -62.904 | cat=landmark_and_historical_building
Brown Station, Antarctic Peninsula                 | lat= -64.892 lon= -62.872 | cat=landmark_and_historical_building
Brown Station                                      | lat= -64.895 lon= -62.870 | cat=landmark_and_historical_building
González Videla Antarctic Base                     | lat= -64.823 lon= -62.858 | cat=airport
Port Lockroy, Antarctic Peninsula                  | lat= -64.825 lon= -63.491 | cat=landmark_and_historical_building
Mount William (Antarctica)                         | lat= -64.783 lon= -63.683 | cat=mountain
Lemaire Channel, Antarctic Peninsula               | lat= -65.103 lon= -63.982 | cat=landmark_and_historical_building
Palmer Station, Antarctica                         | lat= -64.774 lon= -64.053 | cat=home_developer
Mount Jackson (Antarctica)                         | lat= -71.364 lon= -63.412 | cat=mountain
New Lucena Municipal Police Station                | lat= -80.872 lon= -61.348 | cat=public_service_and_government
Esperanto Island, Antarctica                       | lat= -62.641 lon= -60.365 | cat=landmark_and_historical_building
Cierva Cove, Antarctic Peninsula                   | lat= -64.154 lon= -60.905 | cat=landmark_and_historical_building
Antarctica, Antarctic Circle                       | lat= -64.054 lon= -60.932 | cat=landmark_and_historical_building
Mikkelsen Harbour, Antarctic Peninsula             | lat= -63.896 lon= -60.765 | cat=landmark_and_historical_building
Carlini Base                                       | lat= -62.238 lon= -58.667 | cat=arts_and_entertainment
Bellingshausen Russian Antarctic Station           | lat= -62.200 lon= -58.962 | cat=educational_research_institute
King George Bay (Antarctica)                       | lat= -62.100 lon= -58.083 | cat=landmark_and_historical_building
Base Marambio, Antartida Argentina                 | lat= -64.241 lon= -56.620 | cat=central_government_office
Marambio Base                                      | lat= -64.241 lon= -56.627 | cat=airport
Base Antártica Marambio                            | lat= -64.237 lon= -56.624 | cat=public_and_government_association
Chapel of the Blessed Virgin of Lujan, Antarctica  | lat= -64.241 lon= -56.628 | cat=church_cathedral
Esperanza Base, Antarctica                         | lat= -63.398 lon= -56.996 | cat=landmark_and_historical_building
Base Esperanza, Antartida Argentina                | lat= -63.398 lon= -56.997 | cat=airport
British Antarctic Territory                        | lat= -75.000 lon= -50.000 | cat=landmark_and_historical_building
Neumayer-Station II                                | lat= -70.650 lon=  -8.250 | cat=landmark_and_historical_building
Antarctica/Troll                                   | lat= -72.011 lon=   2.535 | cat=landmark_and_historical_building
Fuel Tech Micro Gas Station & Dispensers           | lat= -65.339 lon=  15.469 | cat=industrial_equipment
Plateau Station                                    | lat= -79.250 lon=  40.500 | cat=landmark_and_historical_building
Pioneer - Gas Station                              | lat= -78.910 lon=  43.926 | cat=gas_station
Lake Glubokoye (Antarctica)                        | lat= -67.667 lon=  45.867 | cat=lake
Beaver Lake (Antarctica)                           | lat= -70.783 lon=  68.300 | cat=landmark_and_historical_building
Nicholas Range (Antarctic)                         | lat= -66.667 lon=  55.617 | cat=mountain
Base antarctique Mawson                            | lat= -67.603 lon=  62.874 | cat=landmark_and_historical_building
East Antarctica                                    | lat= -80.000 lon=  80.000 | cat=landmark_and_historical_building
Antarctic Kunlun Station                           | lat= -80.417 lon=  77.116 | cat=landmark_and_historical_building
COFFE Station Old town                             | lat= -84.608 lon= 116.719 | cat=cafe
Base antártica Concordia                           | lat= -75.100 lon= 123.333 | cat=train_station
Base antarctique Concordia                         | lat= -75.100 lon= 123.558 | cat=landmark_and_historical_building
Base antarctique Dumont-d'Urville                  | lat= -66.663 lon= 140.001 | cat=landmark_and_historical_building
Antarctic Plateau                                  | lat= -77.000 lon= 150.000 | cat=landmark_and_historical_building
Lake Bonney (Antarctica)                           | lat= -77.717 lon= 162.417 | cat=lake
Mario-Zucchelli-Station                            | lat= -74.694 lon= 164.112 | cat=?
Base McMurdo                                       | lat= -77.850 lon= 166.667 | cat=landmark_and_historical_building
Base antarctique McMurdo                           | lat= -77.850 lon= 166.667 | cat=landmark_and_historical_building

python -c "
import overturemaps

# Get infrastructure names more carefully
infra = overturemaps.geodataframe('infrastructure', bbox=(-180, -90, 180, -60), release='2026-05-20.0')

for idx, row in infra.iterrows():
    nm = row.get('names')
    p = ''
    if isinstance(nm, dict):
        p = nm.get('primary', '')
    elif isinstance(nm, str):
        p = nm
    lat = row.geometry.y if hasattr(row.geometry, 'y') else row.geometry.centroid.y
    if p and any(kw in p.lower() for kw in ['station', 'base', 'research']):
        print(f'{p:50s} | lat={lat:8.3f} | subtype={row[\"subtype\"]:15s} | class={row[\"class\"]}')
" 2>&1

Marlene Automatic Weather Station                  | lat= -83.650 | subtype=tower           | class=observation
Union Glacier Station                              | lat= -79.759 | subtype=recreation      | class=camp_site
Rothera Research Station Airfield                  | lat= -67.568 | subtype=airport         | class=airport
San Martín Base                                    | lat= -68.117 | subtype=airport         | class=airport
Palmer Station Skiway                              | lat= -64.775 | subtype=airport         | class=airport
Matienzo Base                                      | lat= -64.000 | subtype=airport         | class=airport
Puerto Moro (Base Esperanza)                       | lat= -63.395 | subtype=transit         | class=ferry_terminal
Base Antártica Orcadas                             | lat= -60.738 | subtype=airport         | class=airport
Halley Station Skiway                              | lat= -75.582 | subtype=airport         | class=airport
Skipiste Neumayer-Station                          | lat= -70.646 | subtype=airport         | class=airport
Kohnen Station Skiway                              | lat= -75.001 | subtype=airport         | class=airport
Troll main powerstation                            | lat= -72.012 | subtype=power           | class=generator
Troll backup power station                         | lat= -72.012 | subtype=power           | class=generator
Showa station Skiway                               | lat= -69.020 | subtype=airport         | class=airport
Plateau Station Skiway                             | lat= -79.251 | subtype=airport         | class=airport
Molodezhnaya Station Airfield                      | lat= -67.683 | subtype=airport         | class=airport
Mawson Station Skiway                              | lat= -67.614 | subtype=airport         | class=airport
Bharati Indian Antartic Station                    | lat= -69.409 | subtype=transit         | class=ferry_terminal
Progress Station                                   | lat= -69.379 | subtype=transit         | class=ferry_terminal
Vostok Station Skiway                              | lat= -78.471 | subtype=airport         | class=airport
Vostok Station                                     | lat= -78.465 | subtype=communication   | class=communication_tower
Casey Station Skiway                               | lat= -66.288 | subtype=airport         | class=airport
Station Concordia                                  | lat= -75.106 | subtype=airport         | class=airport
Aeroporto Mario Zucchelli Station                  | lat= -74.699 | subtype=airport         | class=airport
Zucchelli station aerodrome                        | lat= -74.690 | subtype=airport         | class=airport
McMurdo Station - Scott Base                       | lat= -77.848 | subtype=pedestrian      | class=information

python -c "
import overturemaps

# Get ALL place features and ALL infrastructure features to compile stations
places = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
infra = overturemaps.geodataframe('infrastructure', bbox=(-180, -90, 180, -60), release='2026-05-20.0')

# From places: get well-known station names
station_place_names = [
    'Brown Station', 'Palmer Station', 'Carlini Base', 
    'Bellingshausen Russian Antarctic Station',
    'Marambio Base', 'Base Marambio', 'Base Antártica Marambio',
    'Esperanza Base', 'Base Esperanza',
    'Neumayer-Station II', 'Plateau Station',
    'Antarctic Kunlun Station',
    'Base antarctique Mawson', 'Base antarctique Concordia',
    'Base antártica Concordia', 'Base antarctique Dumont-d\'Urville',
    'Mario-Zucchelli-Station', 'Base McMurdo', 'Base antarctique McMurdo',
    'Antarctica/Troll', 'González Videla Antarctic Base',
    'San Martín Base', 'Matienzo Base', 'Base Antártica Orcadas',
    'Union Glacier Station', 'Vostok Station', 'Progress Station',
    'Halley Station', 'Rothera Research Station'
]

# Look for them
for idx, row in places.iterrows():
    nm = row['names']
    if isinstance(nm, dict):
        p = nm.get('primary', '')
        if p in station_place_names:
            print(f'PLACE: {p:50s} | {row.geometry.y:.4f}, {row.geometry.x:.4f}')

for idx, row in infra.iterrows():
    nm = row.get('names')
    p = nm.get('primary', '') if isinstance(nm, dict) else ''
    if p in station_place_names:
        lat = row.geometry.y if hasattr(row.geometry, 'y') else row.geometry.centroid.y
        print(f'INFRA: {p:50s} | {lat:.4f}, {row.geometry.x if hasattr(row.geometry,\"x\") else row.geometry.centroid.x:.4f}')
" 2>&1

PLACE: Brown Station                                      | -64.8950, -62.8700
PLACE: González Videla Antarctic Base                     | -64.8234, -62.8575
PLACE: Carlini Base                                       | -62.2379, -58.6668
PLACE: Bellingshausen Russian Antarctic Station           | -62.1998, -58.9624
PLACE: Marambio Base                                      | -64.2410, -56.6268
PLACE: Base Antártica Marambio                            | -64.2374, -56.6235
PLACE: Neumayer-Station II                                | -70.6500, -8.2500
PLACE: Antarctica/Troll                                   | -72.0114, 2.5350
PLACE: Plateau Station                                    | -79.2500, 40.5000
PLACE: Base antarctique Mawson                            | -67.6026, 62.8736
PLACE: Antarctic Kunlun Station                           | -80.4169, 77.1161
PLACE: Base antártica Concordia                           | -75.1000, 123.3333
PLACE: Base antarctique Concordia                         | -75.1000, 123.5580
PLACE: Base antarctique Dumont-d'Urville                  | -66.6628, 140.0013
PLACE: Mario-Zucchelli-Station                            | -74.6943, 164.1125
PLACE: Base McMurdo                                       | -77.8500, 166.6667
PLACE: Base antarctique McMurdo                           | -77.8500, 166.6667
INFRA: Union Glacier Station                              | -79.7590, -82.8270
INFRA: San Martín Base                                    | -68.1167, -67.1000
INFRA: Matienzo Base                                      | -64.0000, -60.0000
INFRA: Base Antártica Orcadas                             | -60.7381, -44.7377
INFRA: Progress Station                                   | -69.3794, 76.3954
INFRA: Vostok Station                                     | -78.4653, 106.8353

python -c "
import overturemaps

# Get all place features and scan for anything that could be a station
places = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')

# Try to find more stations by looking at all names
def get_primary(nm):
    if isinstance(nm, dict):
        return nm.get('primary', '')
    return ''

places['prim_name'] = places['names'].apply(get_primary)

# Known full station names to look for
known = [
    'McMurdo', 'Scott Base', 'Amundsen-Scott', 'South Pole',
    'Palmer', 'Mawson', 'Davis', 'Casey',
    'Zucchelli', 'Concordia', 'Dumont', 'd\'Urville',
    'Neumayer', 'Halley', 'Rothera',
    'Bellingshausen', 'Progress', 'Vostok', 'Mirny', 
    'Novolazarevskaya', 'Molodezhnaya', 'Bharati', 'Maitri',
    'Jang Bogo', 'Sejong', 'Great Wall', 'Zhongshan', 'Kunlun', 'Taishan',
    'Esperanza', 'Marambio', 'Carlini', 'Orcadas', 'San Martín', 'Brown',
    'Troll', 'Syowa', 'Showa',
    'O\'Higgins', 'Frei', 'Escudero', 'González Videla',
    'Vernadsky', 'Gabriel de Castilla', 'Juan Carlos I',
    'Ohridski', 'Arctowski', 'Ferraz',
    'Artigas', 'Machu Picchu', 'Yelcho',
    'Aboa', 'Wasa', 'Svea', 'SANAE', 'Princess Elisabeth',
    'Kohnen', 'Union Glacier', 'Plateau',
    'Leningradskaya', 'Russkaya', 'Druzhnaya'
]

for idx, row in places.iterrows():
    prim = row['prim_name']
    for k in known:
        if k.lower() in prim.lower():
            cat = row['categories']
            cat_prim = cat.get('primary','?') if isinstance(cat, dict) else '?'
            print(f'{prim:55s} | {row.geometry.y:8.3f}, {row.geometry.x:8.3f} | {cat_prim}')
            break
" 2>&1 | tail -50

Brown Station, Antarctic Peninsula                      |  -64.892,  -62.872 | landmark_and_historical_building
Brown Station                                           |  -64.895,  -62.870 | landmark_and_historical_building
González Videla Antarctic Base                          |  -64.823,  -62.858 | airport
Palmer Station, Antarctica                              |  -64.774,  -64.053 | home_developer
Carlini Base                                            |  -62.238,  -58.667 | arts_and_entertainment
Bellingshausen Russian Antarctic Station                |  -62.200,  -58.962 | educational_research_institute
Base Marambio, Antartida Argentina                      |  -64.241,  -56.620 | central_government_office
Marambio Base                                           |  -64.241,  -56.627 | airport
Base Antártica Marambio                                 |  -64.237,  -56.624 | public_and_government_association
Esperanza Base, Antarctica                              |  -63.398,  -56.996 | landmark_and_historical_building
Base Esperanza, Antartida Argentina                     |  -63.398,  -56.997 | airport
Neumayer-Station II                                     |  -70.650,   -8.250 | landmark_and_historical_building
Troll Airfield                                          |  -71.957,    2.467 | airport
Antarctica/Troll                                        |  -72.011,    2.535 | landmark_and_historical_building
SANAE                                                   |  -72.667,    2.850 | landmark_and_historical_building
Windfreight Express Total Logistics Inc.(WETLI)         |  -75.073,   43.066 | freight_and_cargo_service
Plateau Station                                         |  -79.250,   40.500 | landmark_and_historical_building
EEC Paroisse Plateau Pointe-Noire1                      |  -74.347,   62.531 | evangelical_church
Base antarctique Mawson                                 |  -67.603,   62.874 | landmark_and_historical_building
Antarctic Kunlun Station                                |  -80.417,   77.116 | landmark_and_historical_building
Lake Vostok                                             |  -77.500,  106.000 | landmark_and_historical_building
Vostok İstasyonu                                        |  -78.464,  106.837 | landmark_and_historical_building
Base antártica Concordia                                |  -75.100,  123.333 | train_station
Base antarctique Concordia                              |  -75.100,  123.558 | landmark_and_historical_building
Base antarctique Dumont-d'Urville                       |  -66.663,  140.001 | landmark_and_historical_building
Antarctic Plateau                                       |  -77.000,  150.000 | landmark_and_historical_building
Mario-Zucchelli-Station                                 |  -74.694,  164.112 | ?
McMurdo General Hospital                                |  -77.847,  166.669 | hospital
Base McMurdo                                            |  -77.850,  166.667 | landmark_and_historical_building
Polární stanice McMurdo                                 |  -77.850,  166.667 | landmark_and_historical_building
Base antarctique McMurdo                                |  -77.850,  166.667 | landmark_and_historical_building

python -c "
import overturemaps

# Check for specific station names in the data
places = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
infra = overturemaps.geodataframe('infrastructure', bbox=(-180, -90, 180, -60), release='2026-05-20.0')

second_stations = [
    'McMurdo', 'Scott Base', 'Syowa', '昭和', 'Showa', '昭和基地',
    'Rothera', 'Halley', 'Vernadsky', 'Mirny', 'Novolazarevskaya',
    'Molodezhnaya', 'Bharati', 'Maitri', 'Zhongshan', 'Great Wall',
    '长城站', 'Jang Bogo', 'Sejong', 'Davis', 'Casey',
    'O\'Higgins', 'Frei', 'Escudero', 'Gabriel de Castilla', 'Juan Carlos',
    'Ohridski', 'Arctowski', 'Ferraz', 'Machu Picchu',
    'Aboa', 'Wasa', 'Princess Elisabeth', 'Kohnen',
    'Leningradskaya', 'Russkaya', 'Druzhnaya', 'Artigas',
    'Yelcho', 'Comandante Ferraz', 'King Sejong'
]

for idx, row in places.iterrows():
    nm = row['names']
    prim = nm.get('primary','') if isinstance(nm, dict) else ''
    for k in second_stations:
        if k.lower() in prim.lower():
            print(f'PLACE: {prim:50s} | {row.geometry.y:8.3f}, {row.geometry.x:8.3f}')
            break

for idx, row in infra.iterrows():
    nm = row.get('names')
    prim = nm.get('primary','') if isinstance(nm, dict) else ''
    for k in second_stations:
        if k.lower() in prim.lower():
            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
            print(f'INFRA: {prim:50s} | {lat:8.3f}, {lon:8.3f} | subtype={row[\"subtype\"]}')
            break
" 2>&1

PLACE: 中国南极长城站                                            |  -62.216,  -58.964
PLACE: Windfreight Express Total Logistics Inc.(WETLI)    |  -75.073,   43.066
PLACE: 昭和基地                                               |  -69.006,   39.590
PLACE: McMurdo General Hospital                           |  -77.847,  166.669
PLACE: Base McMurdo                                       |  -77.850,  166.667
PLACE: Polární stanice McMurdo                            |  -77.850,  166.667
PLACE: Base antarctique McMurdo                           |  -77.850,  166.667
INFRA: Rothera Research Station Airfield                  |  -67.568,  -68.128 | subtype=airport
INFRA: Rothera Skiway                                     |  -67.533,  -68.183 | subtype=airport
INFRA: O'Higgins Skiway                                   |  -63.320,  -57.891 | subtype=airport
INFRA: Halley Station Skiway                              |  -75.582,  -26.541 | subtype=airport
INFRA: Kohnen Station Skiway                              |  -75.001,    0.069 | subtype=airport
INFRA: Heliport of Novolazarevskaya                       |  -70.778,   11.829 | subtype=airport
INFRA: Novolazarevskaya Airfield                          |  -70.826,   11.639 | subtype=airport
INFRA: Showa station Skiway                               |  -69.020,   39.625 | subtype=airport
INFRA: Molodezhnaya Station Airfield                      |  -67.683,   46.135 | subtype=airport
INFRA: Bharati Heliport                                   |  -69.407,   76.194 | subtype=airport
INFRA: Bharati Indian Antartic Station                    |  -69.409,   76.209 | subtype=transit
INFRA: Davis Sea Ice Ski Landing Area                     |  -68.568,   77.966 | subtype=airport
INFRA: Davis Sea Ice Ski Landing Area                     |  -68.565,   77.968 | subtype=airport
INFRA: Davis Plateau Ski Landing Area                     |  -68.469,   78.870 | subtype=airport
INFRA: Davis Plateau Ski Landing Area                     |  -68.467,   78.879 | subtype=airport
INFRA: Casey Station Skiway                               |  -66.288,  110.758 | subtype=airport
INFRA: McMurdo Power Plant                                |  -77.847,  166.660 | subtype=power
INFRA: McMurdo Station - Scott Base                       |  -77.848,  166.770 | subtype=pedestrian
INFRA: McMurdo Heliport                                   |  -77.849,  166.669 | subtype=airport

python -c "
import overturemaps

# Let's also check for a few more stations I know should exist
# Check for Syowa/Showa specifically
places = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60), release='2026-05-20.0')
for idx, row in places.iterrows():
    nm = row['names']
    prim = nm.get('primary','') if isinstance(nm, dict) else ''
    if 'syowa' in prim.lower() or 'show' in prim.lower():
        print(f'{prim} | {row.geometry.y:.4f}, {row.geometry.x:.4f}')
    if '昭和' in prim:
        print(f'{prim} | {row.geometry.y:.4f}, {row.geometry.x:.4f}')
    if 'mcmurdo' in prim.lower() and 'base' not in prim.lower():
        print(f'{prim} | {row.geometry.y:.4f}, {row.geometry.x:.4f}')
    if 'south pole' in prim.lower() or 'amundsen' in prim.lower() or 'scott' in prim.lower():
        print(f'{prim} | {row.geometry.y:.4f}, {row.geometry.x:.4f}')
" 2>&1

LosChelosshow | -63.4192, -19.9041
Scott Mountains | -67.1333, 51.3500
Trini Chatterbox for Movie & TV Show Reviews - PAGE | -61.9390, 42.1875
昭和基地 | -69.0061, 39.5900
McMurdo General Hospital | -77.8468, 166.6693
Polární stanice McMurdo | -77.8500, 166.6667

Fetching place features...
Fetching infrastructure features...
Total stations: 53
                                station_name
0   Bellingshausen Russian Antarctic Station
1                               Carlini Base
2                             Esperanza Base
3                              Marambio Base
4                              Brown Station
5                             Palmer Station
6             González Videla Antarctic Base
7                        Neumayer-Station II
8                                      Troll
9                                      SANAE
10                           Plateau Station
11                            Mawson Station
12                  Antarctic Kunlun Station
13                         Concordia Station
14                  Dumont-d'Urville Station
15                   Mario-Zucchelli Station
16                           McMurdo Station
17                             Syowa Station
18                        Great Wall Station
19                  Rothera Research Station
20                            Halley Station
21                     Union Glacier Station
22                           San Martín Base
23                             Matienzo Base
24                              Orcadas Base
25                            Vostok Station
26                          Progress Station
27                      Molodezhnaya Station
28                  Novolazarevskaya Station
29                           Bharati Station
30                            Kohnen Station
31                             Casey Station
32                             Davis Station
33                             Mirny Station
34                         Zhongshan Station
35                         Jang Bogo Station
36                            Sejong Station
37                         O'Higgins Station
38                Princess Elisabeth Station
39                         Arctowski Station
40                          Escudero Station
41               Gabriel de Castilla Station
42                     Juan Carlos I Station
43                              Aboa Station
44                              Wasa Station
45                            Yelcho Station
46                     Macchu Picchu Station
47                            Ferraz Station
48                          Ohridski Station
49                           Artigas Station
50                         Vernadsky Station
51                    Leningradskaya Station
52                          Russkaya Station

Fetching Antarctic landmass...
Land polygon features: 79227
Unified landmass area (deg²): 6662

Fetching water features...
Water features: 13501
Fetching bathymetry features...
Bathymetry features: 3096
Water polygon features: 7455
Bathy polygon features: 3096

Reprojecting to EPSG:3031...
Traceback (most recent call last):
  File "/work/solve.py", line 152, in <module>
    land_proj = antarctica_land.to_crs(ANTARCTIC_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.

Fetching place features...
Fetching infrastructure features...
Total stations: 53
                                station_name
0   Bellingshausen Russian Antarctic Station
1                               Carlini Base
2                             Esperanza Base
3                              Marambio Base
4                              Brown Station
5                             Palmer Station
6             González Videla Antarctic Base
7                        Neumayer-Station II
8                                      Troll
9                                      SANAE
10                           Plateau Station
11                            Mawson Station
12                  Antarctic Kunlun Station
13                         Concordia Station
14                  Dumont-d'Urville Station
15                   Mario-Zucchelli Station
16                           McMurdo Station
17                             Syowa Station
18                        Great Wall Station
19                  Rothera Research Station
20                            Halley Station
21                     Union Glacier Station
22                           San Martín Base
23                             Matienzo Base
24                              Orcadas Base
25                            Vostok Station
26                          Progress Station
27                      Molodezhnaya Station
28                  Novolazarevskaya Station
29                           Bharati Station
30                            Kohnen Station
31                             Casey Station
32                             Davis Station
33                             Mirny Station
34                         Zhongshan Station
35                         Jang Bogo Station
36                            Sejong Station
37                         O'Higgins Station
38                Princess Elisabeth Station
39                         Arctowski Station
40                          Escudero Station
41               Gabriel de Castilla Station
42                     Juan Carlos I Station
43                              Aboa Station
44                              Wasa Station
45                            Yelcho Station
46                     Macchu Picchu Station
47                            Ferraz Station
48                          Ohridski Station
49                           Artigas Station
50                         Vernadsky Station
51                    Leningradskaya Station
52                          Russkaya Station

Fetching Antarctic landmass...
Land polygon features: 79227
/work/solve.py:126: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  print(f"Unified landmass area (deg²): {antarctica_land.area.iloc[0]:.0f}")
Unified landmass area (deg²): 6662

Fetching water features...
Water features: 13501
Fetching bathymetry features...
Bathymetry features: 3096
Water polygon features: 7455
Bathy polygon features: 3096

Reprojecting to EPSG:3031...

Buffering stations...
Buffer examples - area (m²): 125461939622

Clipping buffers to Antarctic landmass...
Land-clipped zones: 53

Grouping overlapping zones into coalitions...
Number of coalitions: 19 (distinct): [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), np.int64(10), np.int64(11), np.int64(12), np.int64(13), np.int64(14), np.int64(15), np.int64(16), np.int64(17), np.int64(18), np.int64(19)]

Writing station_spheres.geoparquet...
Wrote station_spheres.geoparquet with 53 features
Coalition value counts:
coalition
1     23
2      3
3      4
4      1
5      1
6      1
7      1
8      1
9      3
10     2
11     1
12     1
13     1
14     1
15     4
16     1
17     2
18     1
19     1
Name: count, dtype: int64

Computing over-water portions with water attribution...
Combined water features: 10551
Water overlap features: 5342

Writing station_water_overlap.geoparquet...
Wrote station_water_overlap.geoparquet with 5342 features

Done!

cd /work && python -c "
import geopandas as gpd
import pyarrow.parquet as pq

# Check station_spheres.geoparquet
print('=== station_spheres.geoparquet ===')
gdf = gpd.read_parquet('/work/station_spheres.geoparquet')
print(f'Columns: {list(gdf.columns)}')
print(f'CRS: {gdf.crs}')
print(f'Length: {len(gdf)}')
print(f'Geometry types: {gdf.geometry.geom_type.unique()}')
print(f'Coalition distinct values: {gdf[\"coalition\"].nunique()}')
print(f'Coalition values: {sorted(gdf[\"coalition\"].unique())}')
print()
print(gdf.head(10))

print()
print('=== station_water_overlap.geoparquet ===')
gdf2 = gpd.read_parquet('/work/station_water_overlap.geoparquet')
print(f'Columns: {list(gdf2.columns)}')
print(f'CRS: {gdf2.crs}')
print(f'Length: {len(gdf2)}')
print(f'Geometry types: {gdf2.geometry.geom_type.unique()}')
print(f'water_source values: {gdf2[\"water_source\"].unique()}')
print()
print(gdf2.head(10))
"

=== station_spheres.geoparquet ===
Columns: ['station_id', 'station_name', 'coalition', 'geometry']
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}}
Length: 53
Geometry types: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str
Coalition distinct values: 19
Coalition values: [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), np.int64(10), np.int64(11), np.int64(12), np.int64(13), np.int64(14), np.int64(15), np.int64(16), np.int64(17), np.int64(18), np.int64(19)]

                                 station_id  ...                                           geometry
0  bellingshausen_russian_antarctic_station  ...  MULTIPOLYGON (((-2441894.723 1548440.519, -244...
1                              carlini_base  ...  MULTIPOLYGON (((-2429925.869 1559750.327, -243...
2                            esperanza_base  ...  MULTIPOLYGON (((-2447129.008 1403061.463, -246...
3                             marambio_base  ...  MULTIPOLYGON (((-2395472.832 1365954.398, -239...
4                             brown_station  ...  MULTIPOLYGON (((-2266593.373 1243752.854, -226...
5                            palmer_station  ...  MULTIPOLYGON (((-2304538.493 1198606.419, -230...
6            gonzález_videla_antarctic_base  ...  MULTIPOLYGON (((-2273622.832 1248008.561, -227...
7                       neumayer_station_ii  ...  MULTIPOLYGON (((-105430.074 2080269.05, -10830...
8                                     troll  ...  MULTIPOLYGON (((286174.493 1948584.163, 283294...
9                                     sanae  ...  MULTIPOLYGON (((293367.717 1875237.545, 290487...

[10 rows x 4 columns]

=== station_water_overlap.geoparquet ===
Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
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}}
Length: 5342
Geometry types: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str
water_source values: <ArrowStringArray>
['base.bathymetry', 'base.water']
Length: 2, dtype: str

                                 station_id  ...                                           geometry
0  bellingshausen_russian_antarctic_station  ...  MULTIPOLYGON (((-2440011.091 1561138.936, -244...
1  bellingshausen_russian_antarctic_station  ...  MULTIPOLYGON (((-2440011.091 1561138.936, -244...
2  bellingshausen_russian_antarctic_station  ...  MULTIPOLYGON (((-2440011.091 1561138.936, -244...
3  bellingshausen_russian_antarctic_station  ...  MULTIPOLYGON (((-2440011.091 1561138.936, -244...
4  bellingshausen_russian_antarctic_station  ...  MULTIPOLYGON (((-2440011.091 1561138.936, -244...
5  bellingshausen_russian_antarctic_station  ...  MULTIPOLYGON (((-2456630.246 1503904.998, -245...
6  bellingshausen_russian_antarctic_station  ...  MULTIPOLYGON (((-2457662.506 1507525.632, -245...
7  bellingshausen_russian_antarctic_station  ...  MULTIPOLYGON (((-2457623.497 1507486.497, -245...
8  bellingshausen_russian_antarctic_station  ...  MULTIPOLYGON (((-2457623.497 1507486.497, -245...
9  bellingshausen_russian_antarctic_station  ...  MULTIPOLYGON (((-2440011.091 1561138.936, -244...

[10 rows x 7 columns]