Cross-modal retrieval across optical, SAR & multispectral sensors — the map isn't the territory, but the embedding might be.
Contrastive alignment trained on Sentinel-1, Sentinel-2 & Landsat-8 tiles, indexed for sub-50ms search.
A research-grade retrieval platform that finds the same patch of Earth across three different satellites, three different physics, one shared latent space.
First request may take 30–60s — free-tier services cold-start from sleep.
An optical image and a SAR image of the same region are pushed through separate encoders into a common d-dimensional space, so geometry — not sensor physics — becomes the basis for comparison.
Training minimizes embedding distance between images of the same location while enforcing a margin m between unrelated pairs — the core hypothesis of the whole platform.
For a positive pair, the loss softly ranks the true match above every other candidate in the batch, tempered by τ.
A FAISS approximate nearest-neighbour index turns brute-force search into something that stays fast past a million tiles.
| Modality | Source | Bands / Format |
|---|---|---|
| Optical | Sentinel-2 · ESA Copernicus | 10 bands (B02–B12) · 10–60m resolution |
| SAR | Sentinel-1 · ESA Copernicus | VV + VH polarisations · ~10m resolution |
| Multispectral | Landsat-8 · USGS | 11 bands · 30m resolution |
The live demo index is seeded with 20 synthetic tiles across the Amazon, Sahara, Himalayas, Arctic and Pacific — the same /index/add endpoint scales to millions in production.
# 1. clone the repo
git clone https://github.com/Tejaswanth2406/geofusion-platform.git
cd geofusion-platform
pip install -r requirements.txt
# 2. gateway, embedding, retrieval, evaluation, MLflow, Prometheus, Grafana
docker-compose up --build -d
curl -X POST http://localhost:8000/auth/token \
-H "Content-Type: application/json" \
-d '{"username":"admin","password":"geofusion_demo_2026"}'
curl -X POST http://localhost:8000/api/v1/retrieve \
-H "Authorization: Bearer <ACCESS_TOKEN>" \
-F "sensor=optical" \
-F "image=@data/sentinel2/tile001/image.tiff"
Share of the top-K retrieved tiles that are actually relevant.
Share of all relevant tiles the system managed to surface.
Harmonic mean of precision and recall at K.
Mean average precision plus p50/p95/p99 request latency, tracked in Prometheus.
Sign in with the demo credentials above and run a real cross-modal search in under a minute.