SegChange-R1: LLM-Augmented Remote Sensing Change Detection
This work addresses building change detection from UAV viewpoints, offering a domain-specific advancement with a new dataset.
The paper tackles remote sensing change detection by proposing an LLM-augmented inference approach that integrates textual descriptions to guide focus on relevant change regions, achieving significant improvements over existing methods on four datasets.
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}.