D2FLS-Net: Dual-Stage DEM-guided Fusion Transformer for landslide segmentation.

ABSTRACT: Landslide segmentation from remote sensing imagery is crucial for rapid disaster assessment and risk mitigation. Owing to the pronounced heterogeneity of landslide scales and the subtle visual contrast between some landslide bodies and their background, this task remains highly challenging. Although Transformers surpass convolutional neural networks in modeling long-range contextual dependencies, channel-level or feature-level fusion strategies provide only intermittent terrain cues, leading models to underutilize digital elevation model (DEM) information and to lack fine-grained adaptability to terrain variability. To address this, We propose a Swin-Transformer-based framework, Dual-Stage DEM-guided Fusion Transformer for landslide segmentation (D2FLS-Net), which embeds terrain features v

SUBMITTER: Zhao C 

PROVIDER: S-EPMC12654934 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature