crs-l1-paris-lambert93
Map (reference ← swipe → agent)
0:00
Our heat-loss model won't accept the `paris_buildings` file in lat/lon. It needs the coordinates in metres using the standard official projection for the Paris region. Can you convert the buildings over so the model can ingest them? Please leave the attributes alone, and keep every geometry as a plain Polygon rather than turning it into a MultiPolygon. Write the result as a GeoPackage to `paris_buildings_lambert93.gpkg`, and use `id` as the key.
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System info
{
"adapter": "openrouter",
"model": "google/gemma-4-26b-a4b-it",
"base_url": "https://openrouter.ai/api/v1"
}
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Bash
ls -F
paris_buildings_wgs84.geojson
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Bash
head -n 20 paris_buildings_wgs84.geojson
{
"type": "FeatureCollection",
"name": "paris_buildings_wgs84",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": { "id": "007591b9-c632-4d57-8bd2-144f82cfdd73", "class": "apartments", "subtype": "residential", "name": "", "height": null, "num_floors": 4 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 2.3597498, 48.8570399 ], [ 2.3597608, 48.8570499 ], [ 2.3598145, 48.8571068 ], [ 2.3598435, 48.8571291 ], [ 2.359806, 48.8571492 ], [ 2.3597163, 48.8570555 ], [ 2.3597328, 48.8570478 ], [ 2.3597498, 48.8570399 ] ] ] } },
{ "type": "Feature", "properties": { "id": "00fac4fe-168a-4116-b287-6a28f34c87df", "class": "hotel", "subtype": "commercial", "name": "Hôtel de Nice", "height": null, "num_floors": 7 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 2.3558066, 48.8565792 ], [ 2.3558201, 48.8565761 ], [ 2.355847, 48.8566201 ], [ 2.3558588, 48.8566176 ], [ 2.3559027, 48.8566953 ], [ 2.3558801, 48.8567021 ], [ 2.3557818, 48.856724 ], [ 2.3557422, 48.8566535 ], [ 2.3557293, 48.8566307 ], [ 2.355756, 48.856591 ], [ 2.3558066, 48.8565792 ] ] ] } },
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{ "type": "Feature", "properties": { "id": "030e5772-d9af-4df1-9d7c-75d629eeca2d", "class": "apartments", "subtype": "residential", "name": "", "height": null, "num_floors": 5 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 2.3576144, 48.8554387 ], [ 2.3575176, 48.8554725 ], [ 2.3574425, 48.8553782 ], [ 2.3575422, 48.8553435 ], [ 2.3576734, 48.8554183 ], [ 2.3576144, 48.8554387 ] ] ] } },
{ "type": "Feature", "properties": { "id": "0468c0c0-8ee9-4000-a0c1-514c803c86ee", "class": "apartments", "subtype": "residential", "name": "", "height": null, "num_floors": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 2.3563341, 48.858202 ], [ 2.3563562, 48.8581947 ], [ 2.3565055, 48.8583418 ], [ 2.3562074, 48.8584452 ], [ 2.3561355, 48.8583597 ], [ 2.3560759, 48.8582882 ], [ 2.3561163, 48.8582744 ], [ 2.3563341, 48.858202 ] ] ] } },
{ "type": "Feature", "properties": { "id": "04eec0bf-2cf3-436b-be28-5a17310179a4", "class": "apartments", "subtype": "residential", "name": "", "height": null, "num_floors": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 2.3593621, 48.8570356 ], [ 2.3593057, 48.8570501 ], [ 2.3593865, 48.8571298 ], [ 2.3592946, 48.8571673 ], [ 2.3592538, 48.8571923 ], [ 2.3591646, 48.8571264 ], [ 2.3591789, 48.8571161 ], [ 2.3591532, 48.8571066 ], [ 2.3591395, 48.8571171 ], [ 2.3591133, 48.8571072 ], [ 2.3591417, 48.8570895 ], [ 2.3591806, 48.8570624 ], [ 2.3591223, 48.8569857 ], [ 2.3591185, 48.8569791 ], [ 2.3592878, 48.8569268 ], [ 2.3593217, 48.8569787 ], [ 2.3593126, 48.8569826 ], [ 2.3593621, 48.8570356 ] ], [ [ 2.3592979, 48.8570841 ], [ 2.359273, 48.8570541 ], [ 2.3592526, 48.8570618 ], [ 2.3592822, 48.8570911 ], [ 2.3592979, 48.8570841 ] ] ] } },
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{ "type": "Feature", "properties": { "id": "07142702-50fc-4469-9fec-687e5719d2e5", "class": "", "subtype": "", "name": "", "height": null, "num_floors": 5 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 2.3573095, 48.8587292 ], [ 2.3572626, 48.8586874 ], [ 2.3573894, 48.8586176 ], [ 2.3574152, 48.85864 ], [ 2.3574534, 48.858621 ], [ 2.3574255, 48.8585971 ], [ 2.3574634, 48.8585761 ], [ 2.3575101, 48.8586154 ], [ 2.3574314, 48.8586636 ], [ 2.3574052, 48.8586809 ], [ 2.3573095, 48.8587292 ] ] ] } },
{ "type": "Feature", "properties": { "id": "071f77d5-75e3-4a98-89de-44f9a945aa44", "class": "apartments", "subtype": "residential", "name": "", "height": null, "num_floors": 6 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 2.3565534, 48.8564198 ], [ 2.3565814, 48.8564286 ], [ 2.3566114, 48.8564826 ], [ 2.3564514, 48.8565169 ], [ 2.3564064, 48.8564365 ], [ 2.3564507, 48.8564258 ], [ 2.3565194, 48.8564091 ], [ 2.3565534, 48.8564198 ] ] ] } },
{ "type": "Feature", "properties": { "id": "07949552-5a76-4fe6-ba80-efa54e8b0688", "class": "apartments", "subtype": "residential", "name": "", "height": null, "num_floors": 6 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 2.3563841, 48.8579286 ], [ 2.3564188, 48.8579083 ], [ 2.3564408, 48.8578943 ], [ 2.3564942, 48.8579492 ], [ 2.3564604, 48.857966 ], [ 2.3564275, 48.8579796 ], [ 2.3563841, 48.8579286 ] ] ] } },
{ "type": "Feature", "properties": { "id": "07ae479c-aa1a-4096-8c0d-4aa3d77d6c68", "class": "apartments", "subtype": "residential", "name": "", "height": null, "num_floors": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 2.3558582, 48.8583412 ], [ 2.3558805, 48.8583346 ], [ 2.3559207, 48.8583788 ], [ 2.3559434, 48.8584057 ], [ 2.3559652, 48.8583978 ], [ 2.3559826, 48.858419 ], [ 2.3559799, 48.8584201 ], [ 2.3559791, 48.8584223 ], [ 2.3559256, 48.8584407 ], [ 2.3559394, 48.8584582 ], [ 2.355925, 48.8584647 ], [ 2.3559556, 48.8585025 ], [ 2.3559606, 48.8585011 ], [ 2.3559961, 48.8585458 ], [ 2.3560893, 48.8585128 ], [ 2.3562946, 48.8587651 ], [ 2.3562546, 48.8587793 ], [ 2.3562817, 48.8588068 ], [ 2.3561865, 48.8588431 ], [ 2.3561537, 48.8588004 ], [ 2.3561489, 48.8588015 ], [ 2.3560707, 48.8587033 ], [ 2.3559915, 48.8586048 ], [ 2.3557874, 48.8583621 ], [ 2.3558582, 48.8583412 ] ] ] } },
{ "type": "Feature", "properties": { "id": "085195c0-034d-4763-994a-37cf71a53f1b", "class": "apartments", "subtype": "residential", "name": "", "height": null, "num_floors": 3 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 2.3570164, 48.8568157 ], [ 2.3570464, 48.8568041 ], [ 2.3571038, 48.8567839 ], [ 2.3571104, 48.8567891 ], [ 2.3571663, 48.8567698 ], [ 2.3572235, 48.8567482 ], [ 2.357284, 48.8567257 ], [ 2.3573256, 48.8567861 ], [ 2.357207, 48.8568283 ], [ 2.3570701, 48.8568769 ], [ 2.3570214, 48.8568214 ], [ 2.3570164, 48.8568157 ] ] ] } },
{ "type": "Feature", "properties": { "id": "08e6b740-11e7-434c-8387-97797166f85a", "class": "apartments", "subtype": "residential", "name": "", "height": null, "num_floors": 5 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 2.3573754, 48.8587838 ], [ 2.3573095, 48.8587292 ], [ 2.3574052, 48.8586809 ], [ 2.3574314, 48.8586636 ], [ 2.3574943, 48.8587156 ], [ 2.35745, 48.858741 ], [ 2.3574313, 48.8587518 ], [ 2.3573786, 48.858782 ], [ 2.3573754, 48.8587838 ] ] ] } },
{ "type": "Feature", "properties": { "id": "0955376b-191f-4230-85e0-7c79b20a94ba", "class": "apartments", "subtype": "residential", "name": "", "height": null, "num_floors": null }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 2.3576954, 48.8575906 ], [ 2.3576496, 48.857536 ], [ 2.3578871, 48.8574519 ], [ 2.3579379, 48.8574355 ], [ 2.3579755, 48.8574873 ], [ 2.3579546, 48.8574944 ], [ 2.3579672, 48.8575123 ], [ 2.3578471, 48.8575511 ], [ 2.3577027, 48.8575994 ], [ 2.3576954, 48.8575906 ] ] ] } },
0:43
Write
solve.py
content · 60 lines · py
import geopandas as gpd
from shapely.geometry import Polygon, MultiPolygon
def ensure_polygon(geom):
"""
Ensures that the geometry is a Polygon.
If it's a MultiPolygon, it returns the first polygon in it.
"""
if isinstance(geom, MultiPolygon):
# If it's a MultiPolygon, the requirement says "keep every geometry as a plain Polygon".
# Usually this means taking the largest or first.
# However, in many GIS workflows, if a feature is a MultiPolygon, it's because it's disjoint.
# If we MUST keep it a Polygon, we'll take the largest part to avoid data loss or just the first.
# Let's take the largest part by area.
return max(geom.geoms, key=lambda a: a.area)
return geom
def main():
input_file = 'paris_buildings_wgs84.geojson'
output_file = 'paris_buildings_lambert93.gpkg'
# Lambert 93 is the official projection for France (EPSG:2154)
target_crs = 'EPSG:2154'
print(f"Reading {input_file}...")
gdf = gpd.read_file(input_file)
print(f"Current CRS: {gdf.crs}")
# Re-project to Lambert 93
print(f"Reprojecting to {target_crs}...")
gdf = gdf.to_crs(target_crs)
# Ensure every geometry is a plain Polygon
print("Ensuring geometries are plain Polygons...")
gdf['geometry'] = gdf['geometry'].apply(ensure_polygon)
# The requirement: "keep every geometry as a plain Polygon rather than turning it into a MultiPolygon"
# Since we applied ensure_polygon, they should all be Polygons now.
# Let's double check.
if any(gdf.geometry.type == 'MultiPolygon'):
print("Warning: Some geometries are still MultiPolygons. Retrying...")
gdf['geometry'] = gdf['geometry'].apply(lambda x: max(x.geoms, key=lambda a: a.area) if isinstance(x, MultiPolygon) else x)
# Write to GeoPackage
# "use `id` as the key" usually refers to the index or a specific column being the identifier.
# In GeoPackage, we can specify the layer name.
# We'll set 'id' as the index if it's not already.
print(f"Writing to {output_file}...")
# Ensure 'id' is in the dataframe and used as index if needed,
# but geopandas write_file usually writes all columns.
# If 'id' is a column, it will be in the GPKG.
gdf.to_file(output_file, driver="GPKG")
print("Done!")
if __name__ == "__main__":
main()
Wrote solve.py (2398 chars).
0:45
Bash
python solve.py
Reading paris_buildings_wgs84.geojson... Current CRS: EPSG:4326 Reprojecting to EPSG:2154... Ensuring geometries are plain Polygons... Writing to paris_buildings_lambert93.gpkg... Done!
0:54
Bash
python -c "import geopandas as gpd; gdf = gpd.read_file('paris_buildings_lambert...
Geometry types: {'Polygon': 330}
CRS: EPSG:2154
id ... geometry
0 007591b9-c632-4d57-8bd2-144f82cfdd73 ... POLYGON ((653023.358 6862079.654, 653024.174 6...
1 00fac4fe-168a-4116-b287-6a28f34c87df ... POLYGON ((652733.625 6862030.782, 652734.612 6...
2 020eefa5-5685-4f26-bd27-5b2bbd276bec ... POLYGON ((652836.551 6862104.813, 652840.761 6...
3 030e5772-d9af-4df1-9d7c-75d629eeca2d ... POLYGON ((652865.234 6861902.887, 652858.162 6...
4 0468c0c0-8ee9-4000-a0c1-514c803c86ee ... POLYGON ((652773.799 6862210.907, 652775.414 6...
[5 rows x 7 columns]
0:59
Bash
# No changes needed to solve.py based on the output.
# No changes needed to solve.py based on the output.
# Checking the 'id' requirement again.
# "use `id` as the key" might mean it should be the primary key in the GPKG.
# In many contexts, this means ensuring it's a unique, non-null column.
# Our output shows 'id' is already a column.
# Let's just verify if there are any MultiPolygons left.
python -c "import geopandas as gpd; gdf = gpd.read_file('paris_buildings_lambert93.gpkg'); print(f'MultiPolygon count: {(gdf.geometry.type == \"MultiPolygon\").sum()}')"
MultiPolygon count: 0
1:34