crs-l2-svalbard-polar-areas

Rerun

import geopandas as gpd

def inspect_data(file_path):
    gdf = gpd.read_file(file_path)
    print("Columns:", gdf.columns.tolist())
    print("CRS:", gdf.crs)
    print("Head:\n", gdf.head())
    print("Geometry type:", gdf.geometry.type.unique())

if __name__ == "__main__":
    inspect_data("/work/svalbard_glaciers_wgs84.gpkg")

import geopandas as gpd
import pandas as pd

def solve():
    # Load the data
    gdf = gpd.read_file("/work/svalbard_glaciers_wgs84.gpkg")
    
    # Svalbard is roughly between 74N and 81N.
    # For area measurements in high latitudes, we should use an Equal Area projection.
    # NSIDC Sea Ice Polar Stereographic North (EPSG:3413) is a good candidate for the Arctic.
    # Alternatively, an Albers Equal Area projection centered on Svalbard.
    # Let's try EPSG:3413 (NSIDC Sea Ice Polar Stereographic North) 
    # or EPSG:3031 (WGS 84 / Antarctic?) No, that's South.
    # EPSG:3413 is standard for polar regions.
    # Another option: EPSG:3995 (WGS 84 / NSIDC Sea Ice Polar Stereographic North)
    # Let's use EPSG:3413.
    
    target_crs = "EPSG:3413"
    
    # Project to the chosen CRS
    gdf_projected = gdf.to_crs(target_crs)
    
    # Calculate area in square meters, then convert to km2
    gdf_projected['area_km2'] = gdf_projected.geometry.area / 10**6
    
    # Get bounding boxes in the projected CRS
    # bbox returns (minx, miny, maxx, maxy)
    bounds = gdf_projected.geometry.bounds
    gdf_projected['bbox_minx_polar'] = bounds['minx']
    gdf_projected['bbox_miny_polar'] = bounds['miny']
    gdf_projected['bbox_maxx_polar'] = bounds['maxx']
    gdf_projected['bbox_maxy_polar'] = bounds['maxy']
    gdf_projected['crs_epsg'] = 3413
    
    # Sort by area descending and take top 20
    top_20 = gdf_projected.sort_values(by='area_km2', ascending=False).head(20)
    
    # Select required columns
    # name, area_km2, bbox_minx_polar, bbox_miny_polar, bbox_maxx_polar, bbox_maxy_polar, crs_epsg
    output_cols = [
        'name', 'area_km2', 'bbox_minx_polar', 'bbox_miny_polar', 
        'bbox_maxx_polar', 'bbox_maxy_polar', 'crs_epsg'
    ]
    
    # Final check on names - the column is 'name'
    result = top_20[output_cols]
    
    # Write to CSV
    result.to_csv("/work/svalbard_glaciers_top20.csv", index=False)
    print("Successfully created /work/svalbard_glaciers_top20.csv")

if __name__ == "__main__":
    solve()