[2605.05344] Open-SAT: LLM-Guided Query Embedding Refinement for Open-Vocabulary Object Retrieval in Satellite Imagery
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.05344 (cs)
[Submitted on 6 May 2026]
Title:Open-SAT: LLM-Guided Query Embedding Refinement for Open-Vocabulary Object Retrieval in Satellite Imagery
Authors:Md Adnan Arefeen, Biplob Debnath, Ravi K. Rajendran, Murugan Sankaradas, Srimat T. Chakradhar
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Abstract:In satellite applications, user queries often take the form of open-ended natural language, extending beyond a fixed set of predefined categories. This open-vocabulary nature poses significant challenges for retrieving relevant image tiles, as the retrieval system must generalize to a wide range of unseen objects and concepts. While vision-language models (VLMs) such as CLIP are widely used for text-image retrieval, even fine-tuned variants often struggle to accurately align such queries with satellite imagery. To address this, we propose Open-SAT, a training-free query embedding refinement algorithm that operates at inference time to improve alignment between user queries and satellite image content. Open-SAT uses VLMs to compute embeddings for image tiles, which are stored in a vector database for efficient retrieval. At query time, it leverages Large Language Models (LLMs) to refine the text embeddings by incorporating contextual information about objects of interest and their surroundings. A threshold-free retrieval mechanism further enhances accuracy and efficiency. Experimental results in three public benchmarks demonstrate that Open-SAT improves the F1 score by up to 16.04%, while retrieving a comparable number of image tiles. These results demonstrate the effectiveness of Open-SAT in open-vocabulary satellite image retrieval, leveraging LLM guidance without the need for additional training or supervision.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
Cite as: arXiv:2605.05344 [cs.CV]
(or arXiv:2605.05344v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.05344
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Md Adnan Arefeen [view email] [v1] Wed, 6 May 2026 18:12:47 UTC (5,553 KB)
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