overture-data
Skill78 starsChanged 8 months agoApache-2.0View on GitHub
Download Overture Maps data (buildings, places, roads, land use, water, etc.) for a bounding box. Returns a GeoDataFrame saved as GeoJSON or GeoPackage.
- Installs packages
1 other project uses a version of this, 1 of them edited.
Adopted by
Most recently taken up by
- opengeos/geoai-skillsedited, first seen Mar 20, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 1
- opengeos/geoai-skills★ 3195% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository19 other files it gives its agents.
- .github/copilot-instructions.mdCopilot instructions
- detect-objectsSkill
- gdalSkill
- geoparquet-validationSkill
- geospatial-frontendSkill
- geospatial-viewersSkill
- geozarrSkill
- parallel-geospatialSkill
- planet-bulk-downloadSkill
- pmtiles-pipelineSkill
- portolan-bootstrapSkill
- portolan-cliSkill
- portolan-thumbnailsSkill
- reading-portolanSkill
- register-catalogSkill
- search-stacSkill
- sourcecoopSkill
- tesseraSkill
- zarr-multiscalesSkill
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 5 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 14
Follow these steps in order.
- Distinctive: line 28
If the data type is not recognized, print the list of valid types and ask the user to pick one.
- Distinctive: line 30
If the user provided natural language (e.g.
- Distinctive: line 38
If validation fails, report the issue and ask for corrected coordinates.
- Distinctive: line 87
If a state directory exists, update it:
- Very specific: line 97
If STATE_DIR is set:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
You are helping the user download Overture Maps data using geoai.
Input: $@
Follow these steps in order.
Step 1 -- Parse arguments
Extract:
$0or the first positional argument as the Overture data type--bbox minx,miny,maxx,maxyas the bounding box (required)--output FILEas the output file path (optional, default:./<data_type>_overture.gpkg)
Valid Overture data types:
address, building, building_part, division, division_area,
division_boundary, place, segment, connector, infrastructure,
land, land_cover, land_use, water
If the data type is not recognized, print the list of valid types and ask the user to pick one.
If the user provided natural language (e.g. "get buildings in downtown Nashville"), extract the data type and either infer or ask for the bounding box.
Step 2 -- Validate the bounding box
Confirm the bounding box has 4 numeric values:
minx < maxxandminy < maxy- Values within WGS84 range
If validation fails, report the issue and ask for corrected coordinates.
Step 3 -- Download the data
For building data specifically
python3 -c "
import geoai
gdf = geoai.download_overture_buildings(
bbox=(MINX, MINY, MAXX, MAXY),
output='OUTPUT_PATH',
)
print(f'Features: {len(gdf)}')
print(f'Columns: {list(gdf.columns)}')
print(f'CRS: {gdf.crs}')
print(f'Bounds: {gdf.total_bounds.tolist()}')
print('---')
print('Sample (first 5 rows):')
print(gdf.head().to_string())
"For all other data types
python3 -c "
import geoai
gdf = geoai.get_overture_data(
overture_type='DATA_TYPE',
bbox=(MINX, MINY, MAXX, MAXY),
output='OUTPUT_PATH',
)
print(f'Features: {len(gdf)}')
print(f'Columns: {list(gdf.columns)}')
print(f'CRS: {gdf.crs}')
print(f'Bounds: {gdf.total_bounds.tolist()}')
print('---')
print('Sample (first 5 rows):')
print(gdf.head().to_string())
"Replace DATA_TYPE, MINX, MINY, MAXX, MAXY, and OUTPUT_PATH with actual values.
Step 4 -- Update state
If a state directory exists, update it:
STATE_DIR=""
test -f .geoai-skills/state.json && STATE_DIR=".geoai-skills"
PROJECT_ROOT="$(git rev-parse --show-toplevel 2>/dev/null || echo "$PWD")"
PROJECT_ID="$(echo "$PROJECT_ROOT" | tr '/' '-')"
test -f "$HOME/.geoai-skills/$PROJECT_ID/state.json" && STATE_DIR="$HOME/.geoai-skills/$PROJECT_ID"If STATE_DIR is set:
python3 -c "
import json, os
state_file = 'STATE_DIR/state.json'
state = {}
if os.path.exists(state_file):
with open(state_file) as f:
state = json.load(f)
state.setdefault('downloaded_files', [])
state['downloaded_files'].append('OUTPUT_PATH')
with open(state_file, 'w') as f:
json.dump(state, f, indent=2)
"Step 5 -- Report results
Summarize:
- Data type downloaded
- Number of features
- Output file path and size
- Column summary
- CRS and spatial extent
Then suggest: "Use the gdal skill to examine the downloaded data, or the geospatial-viewers skill to view it."
Error handling
import geoaifails -> suggestpip install "geoai-py[extra]"(the Overture helpers need the extra).overturemapsnot installed -> suggestpip install "geoai-py[extra]"which includes the overturemaps dependency.- No features found -> suggest expanding the bounding box or trying a different data type.
- Network error -> report and suggest retrying.
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