Enhancing Sentinel-2 landslide change detection by integrating multispectral, deformation, and topographic information

• A novel DMLCDNet integrates multimodal features for landslide change detection. • DMLCDNet improves F1 and IoU by 3.61% and 6.91% over state-of-the-art baselines. • Experiments on multi-type landslides demonstrate robust generalization and efficiency. • Three criteria for optimal input data selection discovered. Landslides are among the most frequent natural hazards worldwide, posing severe risks to human lives and assets. Sentinel-2 imagery, a widely available remote sensing resource, has become an essential tool for detecting landslide changes. Nevertheless, its moderate spatial resolution often causes the loss of subtle surface information, which limits the precision of landslide recognition. To overcome this limitation, this study develops a Dynamic Multi-branch Landslide Change Detection Network (DMLCDNet) that integrates three types of multimodal data: multispectral, deformation, and topographic information. The proposed model achieves efficient landslide extraction through four key stages: preliminary change detection, dynamic feature extraction, feature fusion optimization, and spatial-semantic restoration. Experiments were conducted on three representative landslide events—Jiuzhaigou, Luding, and Longyan. The experimental findings indicate that DMLCDNet achieves improvements of 3.61% in F1-score and 6.91% in IoU over all baseline models, while sustaining efficient inference performance. In addition, evaluations on two other landslide cases confirm the model’s robust generalization ability. The study further emphasizes that the optimal selection of input data should follow three guiding principles: rich information, high discriminability, and strong relevance. Overall, the proposed method provides an effective paradigm for multisource feature fusion in Sentinel-2–based landslide change detection, offering substantial theoretical significance and practical value. The source code and dataset are publicly available at https://github.com/Trifurs/DMLCDNet.