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arXiv · 2506.17944

SegChange-R1: LLM-Augmented Remote Sensing Change Detection

Fei Zhou
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Abstract

Remote sensing change detection is used in urban planning, terrain analysis, and environmental monitoring by analyzing feature changes in the same area over time. In this paper, we propose a large language model (LLM) augmented inference approach (SegChange-R1), which enhances the detection capability by integrating textual descriptive information and guides the model to focus on relevant change regions, accelerating convergence. We designed a linear attention-based spatial transformation module (BEV) to address modal misalignment by unifying features from different times into a BEV space. Furthermore, we introduce DVCD, a novel dataset for building change detection from UAV viewpoints. Experiments on four widely-used datasets demonstrate significant improvements over existing method The code and pre-trained models are available in {https://github.com/Yu-Zhouz/SegChange-R1}.

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Fei Zhou. 2025-06-22. SegChange-R1: LLM-Augmented Remote Sensing Change Detection. https://arxiv.org/abs/2506.17944

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