Overture — multi-theme download (live)¶

A single request can pull several themes / types at once; one file is written per feature type. Here we grab places and building footprints for the same block, pinning a release for reproducibility.

Setup¶

Pull in the pieces we need: Catalog to discover the latest Overture release, the EarthLens entry point to run the download, and geopandas to inspect the written GeoParquet files.

In [ ]:

import geopandas as gpd

from earthlens.earthlens import EarthLens
from earthlens.overture import Catalog

import geopandas as gpd from earthlens.earthlens import EarthLens from earthlens.overture import Catalog

Area of interest¶

Define a small Times Square block as a [south, north] / [west, east] bounding box, and the directory the output files will be written to.

In [ ]:

LAT_LIM = [40.757, 40.759]  # [south, north]
LON_LIM = [-73.987, -73.984]  # [west, east] — a Times Square block
OUT = "_overture_out"

LAT_LIM = [40.757, 40.759] # [south, north] LON_LIM = [-73.987, -73.984] # [west, east] — a Times Square block OUT = "_overture_out"

Pin a release¶

Resolve the most recent Overture release id and pin it, so the download is reproducible even after Overture publishes a newer snapshot.

In [ ]:

catalog = Catalog()
release = catalog.latest_release()  # pin for reproducibility
release

catalog = Catalog() release = catalog.latest_release() # pin for reproducibility release

Download both themes¶

Request places and buildings in one call. Overture writes one GeoParquet file per feature type, so download() returns a list of paths — one per type.

In [ ]:

engine = EarthLens(
    data_source="overture",
    variables={"places": [], "buildings": []},
    aoi=[LON_LIM[0], LAT_LIM[0], LON_LIM[1], LAT_LIM[1]],
    path=OUT,
    release=release,
)
paths = engine.download()
[p.name for p in paths]

engine = EarthLens( data_source="overture", variables={"places": [], "buildings": []}, aoi=[LON_LIM[0], LAT_LIM[0], LON_LIM[1], LAT_LIM[1]], path=OUT, release=release, ) paths = engine.download() [p.name for p in paths]

Inspect the results¶

Read each written file back with geopandas and report its feature count and the set of per-row license ids (buildings are OSM-derived ODbL-1.0; places carry permissive licenses).

In [ ]:

for p in paths:
    gdf = gpd.read_parquet(p)
    print(f'{p.name}: {len(gdf)} features, licenses={sorted(gdf.license_id.unique())}')

for p in paths: gdf = gpd.read_parquet(p) print(f'{p.name}: {len(gdf)} features, licenses={sorted(gdf.license_id.unique())}')