---
license: apache-2.0
tags:
  - geospatial
  - location-encoding
  - contrastive-learning
  - openstreetmap
  - graph-neural-network
  - siren
  - clip
pipeline_tag: feature-extraction
---

# OSMGraphCLIP-A-L10

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-L10** variant: single-scale (non-multiscale) spherical-harmonic bands with Legendre polynomial degree 10.

## 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-L40) are available in the [GitHub repository](https://github.com/d-michail/osmgraphclip).

## 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:
- `satclip_locations.csv` — primary location set
- `h3_locations.csv` — H3-sampled globally-uniform locations

For each location, an OSM graph was fetched and used as the graph encoder's input during training.

## How to use

Install the package from the [GitHub repository](https://github.com/d-michail/osmgraphclip):

```bash
pip install git+https://github.com/d-michail/osmgraphclip.git
```

**Python API:**

```python
import torch
from osmgraphclip.load import get_osmgraphclip_from_hf

# Load the location encoder (no OSM data needed at inference)
location_encoder = get_osmgraphclip_from_hf("osmgraphclip-a-l10", device="cpu")

# coords: tensor of shape (N, 2) in (lon, lat) order
coords = torch.tensor([[13.40, 52.52]])   # Berlin
embedding = location_encoder(coords)      # (N, D)
```

**Command-line:**

```bash
python infer.py --hf-model osmgraphclip-a-l10 --lat 52.52 --lon 13.40
```

> **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
@article{michail2026osmgraphclip,
  title   = {OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs},
  author  = {Michail, Dimitrios and Saka, Eleni and Giannopoulos, Ioannis and Papoutsis, Ioannis},
  journal = {arXiv preprint arXiv:2606.08046},
  year    = {2026}
}
```