Multi-scale differential network for landslide extraction from remote sensing images with different scenarios

Landslides are major geological hazards globally, causing significant economic losses each year. Accurate landslide detection is essential for disaster prevention, risk assessment, and timely emergency response. Current extraction methods struggle to distinguish landslides from their surroundings and precisely define their boundaries. To address these challenges, we introduce the Multi-Scale Difference Enhancement Network (MSDENet), a framework for landslide extraction through time-based change detection. MSDENet incorporates three core components: the Difference Guided Attention Module (DGAM) for enhanced focus on landslide-specific changes, the Multi-Scale Feature Fusion Module (MSFFM) for improved boundary delineation, and the Multi-Scale Sensory Module (MSSM) to boost generalization by integrating multi-scale features. We validate MSDENet’s effectiveness on the Global Very-High-Resolution Landslide Mapping (GVLM) dataset, covering 17 diverse landslide events, and further assess its applicability on high-resolution Nepal and Wenchuan datasets. MSDENet outperforms six contemporary frameworks, achieving IoU improvements of 1.42% and 1.08% for the Kaikoura and Tbilisi datasets and demonstrating gains of 3.97% and 4.79% for the Nepal and Wenchuan datasets, confirming its effectiveness in varied conditions.