ChangeVLM: A Language-Guided Semantic Alignment Framework for Binary Remote Sensing Change Detection

Against the backdrop of complex features and spectral heterogeneity in high-resolution remote sensing imagery, traditional methods suffer from insufficient semantic understanding, while existing vision–language change detection models face low efficiency, poor spatial localization, and decoupled detection–description pipelines. To overcome these limitations, this paper proposes ChangeVLM, a language-guided semantic alignment framework for binary remote sensing change detection, enabling end-to-end, prompt-free, highly efficient, and interpretable change detection. Its key advantages include the following, (1) Higher detection accuracy with F1 scores of 91.52%, 83.56%, and 75.29% on LEVIR-CD, SYSU-ChangeDet, and HRCUS datasets, outperforming 18 state-of-the-art methods. (2) Stronger edge integrity and small-object detection capability; (3) practical deployment efficiency: the end-to-end FLOPs is 560.7G. Additionally, under an optimized inference setting with pre-extracted features, the effective computation can be reduced to 13.05G. (4) Language-guided semantic regularization to enhance visual discrimination, without requiring external text prompts. The Asymmetric Fusion Module (AFM), lightweight ChangeHead, and Change-Aware Cross-Modal Fusion Module (CACMF) jointly enhance spatial precision, efficiency, and interpretability. Extensive experiments validate that ChangeVLM achieves a superior accuracy–efficiency trade-off. This method provides an effective, deployable solution for high-resolution remote sensing binary change detection, where the language branch acts only as a regularization signal.