maz2580/geospark
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Verifiable spatial reasoning for LLMs: geodesic distance, topology and CRS computed by a geometry engine, with sources and licences on every answer. MCP server, REST API, Python library.
概要
GeoSpark Verifiable spatial reasoning for large language models. Distances, topology, coordinate reference systems and heights computed by a geometry engine and official geodetic models, not estimated by a language model. Open source, self-hostable, and usable by any model that can call a tool. GeoSpark provides language models with tools that spatial answers rather than estimate them: geodesic distances, exact topology, official datum and height conversions, geocoding, elevation, routing, open map data and live environmental data. Each result carries its sources, licences, units and caveats, and a result that cannot be computed reliably is reported as such rather than approximated. GeoSpark also checks users' own datasets (vector files, LiDAR point clouds and elevation models) for the silent coordinate and height errors that most often corrupt spatial analyses. GeoSpark is evaluated against the models in current use, and its scope follows the gaps that those evaluations reveal.
README
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GeoSpark Verifiable spatial reasoning for large language models. Distances, topology, coordinate reference systems and heights computed by a geometry engine and official geodetic models, not estimated by a language model. Open source, self-hostable, and usable by any model that can call a tool.
GeoSpark provides language models with tools that compute spatial answers rather than estimate them: geodesic distances, exact topology, official datum and height conversions, geocoding, elevation, routing, open map data and live environmental data. Each result carries its sources, licences, units and caveats, and a result that cannot be computed reliably is reported as such rather than approximated. GeoSpark also checks users’ own datasets (vector files, LiDAR point clouds and elevation models) for the silent coordinate and height errors that most often corrupt spatial analyses.
Contents: Motivation and evaluation history · Capabilities · Access and installation · Usage · Command-line interface · Evaluation · Validation against authoritative sources · Limitations · Self-hosting · Release history · Data attribution · Development · Citation
Motivation and evaluation history
GeoSpark is evaluated against the models in current use, and its scope follows the gaps that those evaluations reveal.
- GeoSpark Bench v2. Across nine models of the time, small open-weight models (3.8B–9B parameters) answered numeric distance questions incorrectly about 95% of the time, and frontier APIs ranged from 25% to 95% correct. With GeoSpark’s tools, every model that issued valid tool calls answered 76–96% correctly; a 7B model running on a CPU improved from 5% to 76%.
インストール
docker run -p 8000:8000 ghcr.io/maz2580/geospark:latest設定
{ "mcpServers": { "geospark": { "command": "geospark-mcp" } } }