[2605.05405] Zero-Shot Satellite Image Retrieval through Joint Embeddings: Application to Crisis Response
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.05405 (cs)
[Submitted on 6 May 2026]
Title:Zero-Shot Satellite Image Retrieval through Joint Embeddings: Application to Crisis Response
Authors:James Walsh, William Fawcett, Grace Colvard, Raúl Ramos-Pollán
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Abstract: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.
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Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.05405 [cs.CV]
(or arXiv:2605.05405v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.05405
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From: William Fawcett [view email] [v1] Wed, 6 May 2026 19:43:44 UTC (7,351 KB)
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