Benchmarking Composed Image Retrieval for Applied Earth Observation
Summary
This paper presents a unified benchmark for composed image retrieval in Earth observation, evaluating vision-language backbones and introducing a change-centric dataset (xView2-CIR) for disaster monitoring, highlighting distinct challenges compared to attribute-based retrieval.
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Paper page - Benchmarking Composed Image Retrieval for Applied Earth Observation
Source: https://huggingface.co/papers/2605.24442
Abstract
Remote sensing composed image retrieval methods are evaluated across vision-language backbones and a new change-centric dataset, demonstrating their effectiveness for Earth observation applications while highlighting distinct challenges compared to traditional attribute-based retrieval.
Remote sensing composed image retrieval(RSCIR) enables search in large satellite image archives using composed queries that combine a reference image with a textual modifier. Although RSCIR offers a flexible interface for expressing targeted retrieval intent, the transferability of modern composition methods to Earth observation (EO) imagery and their relevance to operational EO workflows remain underexplored. We address this gap through a unified benchmark and an application-oriented study. First, we systematically adapt and evaluate representative composed image retrieval methods with sixvision-language backbonesonPatternComunder a standardized protocol, analyzing their behavior across backbones, composition strategies, and query types. Second, we introducexView2-CIR, a change-centric dataset for disaster and damage monitoring, where retrieval is conditioned onscene identityand a target post-event state. Our results show that training-free composition methods provide strong and scalable baselines for EO retrieval, whilechange-centric retrievalpresents different challenges fromattribute-based retrieval, particularly due to the need to preservescene identity. Overall, this study establishes a practical benchmark for RSCIR and positions composed retrieval as a complementary tool for remote sensing image retrieval, archive exploration, and change analysis. The dataset and code are available at https://github.com/billpsomas/rscir.
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