SegChange-R1: LLM-Augmented Remote Sensing Change Detection

• URL: http://arxiv.org/abs/2506.17944v2
• Date: Fri, 27 Jun 2025 13:30:09 GMT
• Title: SegChange-R1: LLM-Augmented Remote Sensing Change Detection
• Authors: Fei Zhou,
• Abstract summary: We propose a large language model (LLM) augmented inference approach (SegChange-R1)<n>We designed a linear attention-based spatial transformation module (BEV) to address modal misalignment.<n>Experiments on four widely-used datasets demonstrate significant improvements over existing method.
• Score: 7.156844376973501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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