A pretrained location encoder from the OSMGraphCLIP framework. It maps geographic coordinates (longitude, latitude) to dense vector embeddings that capture the semantic character of a location — its land use, built environment, road network, and landscape context — learned from freely available OpenStreetMap data.
This is the A-L40 variant: single-scale (non-multiscale) spherical-harmonic bands with Legendre polynomial degree 40.
Model description
OSMGraphCLIP trains a CLIP-style contrastive model that aligns two views of a location:
Graph encoder (OSMHeteroGAT): processes a heterogeneous OSM graph (points, lines, polygons — roads, buildings, land use, POIs) centered at the location, using SBERT node features.
Location encoder: maps geographic coordinates through spherical-harmonic positional encodings and a SIREN network.
Symmetric cross-entropy loss aligns matching graph–coordinate pairs into a shared embedding space. After training, the location encoder alone is sufficient for inference — no OSM data is needed at query time. The graph encoder is only used during training.
Other pretrained variants (MS-L40, MS-L10, A-L10) are available in the GitHub repository.
Intended uses
Geographic/geospatial representation learning
Downstream prediction tasks: climate, ecology, socioeconomics, public health, land cover, biodiversity, wildfire forecasting
Location-conditioned retrieval or similarity search
Any task that benefits from a semantically rich, globally consistent coordinate embedding
Training data
Approximately 200,000 globally-diverse locations sampled from:
Note: coordinates must be provided in (longitude, latitude) order.
Evaluation
On a suite of downstream geospatial tasks (climate, ecology, socioeconomics, public health, land cover, biodiversity, wildfire forecasting), OSMGraphCLIP performs competitively with or surpasses satellite-imagery baselines. It shows particular strength on socioeconomic and public health tasks, where OSM's semantic annotations of the human-built environment offer an advantage over pixel-based approaches. Qualitative analysis shows that the learned embeddings coherently organise geographic space, recovering biome boundaries and urban-to-rural gradients.
Citation
bibtex
1@article{michail2026osmgraphclip,
2 title = {OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs},
3 author = {Michail, Dimitrios and Saka, Eleni and Giannopoulos, Ioannis and Papoutsis, Ioannis},
4 journal = {arXiv preprint arXiv:2606.08046},
5 year = {2026}
6}
