Zero-Shot Satellite Image Retrieval through Joint Embeddings: Application to Crisis Response
Provides a practical zero-shot retrieval method for crisis response when full contrastive training is infeasible.
GeoQuery achieves 31.6% accuracy within 50 km on 76 disaster-location queries by using prompt-aligned text proxies to bridge frozen visual embeddings and natural language, avoiding costly contrastive training.
Semantic search of Earth observation archives remains challenging. Visual foundation models such as CLAY produce rich embeddings of satellite imagery but lack the natural-language grounding needed for intuitive query, and full contrastive training of a remote-sensing CLIP-style model requires paired data and compute that are unavailable at global scale. We present GeoQuery, a zero-shot retrieval system that sidesteps this constraint through prompt-aligned text proxies. Rather than training a joint encoder, we generate language descriptions for a 100k proxy subset of global Sentinel-2 tiles and optimise the description-generation prompt so that distances in the resulting text-embedding space correlate with distances in the frozen CLAY visual-embedding space. Queries are resolved in two stages, with a text-similarity search over the proxy subset followed by a visual nearest-neighbour search over worldwide CLAY embeddings. On 76 disaster-location queries covering UK floods, US wildfires, and US droughts, GeoQuery achieves 31.6% accuracy within 50 km, with the strongest performance on floods (50% within 50 km) where terrain features are well captured by RGB embeddings. Deployed within ECHO, a crisis response system using Agentic Action Graphs, GeoQuery identified vulnerable areas during Brisbane's 2025 Cyclone Alfred, with downstream flood simulations reproducing historical patterns. Prompt-aligned proxies offer a practical bridge between EO foundation models and operational retrieval when full contrastive training is out of reach.