MMCA-SAM: Multimodal Cross-Attention SAM for Landslide Segmentation with Topographic Guidance

Abstract

Landslides are a common, destructive form of geological disaster that poses a threat to both infrastructure and human life. The Segment Anything Model (SAM) is a strong segmentation model, but it still has difficulty with the unclear boundaries and complex terrain that are typical of landslides. Most existing multimodal models also have difficulty in deeply fusing Digital Elevation Models (DEM) and optical images. To address these issues, we introduce MMCA-SAM, a terrain-aware multimodal segmentation model. MMCA-SAM incorporates a Cross-Attention Fusion Module (CAFM) to align RGB semantics with terrain geometry. It also incorporates Atrous Spatial Pyramid Pooling (ASPP) and a decoder with Coordinate Attention (CA) to improve the resolution of unclear boundaries. Experiments on the Bijie and Landslide4Sense datasets demonstrate that MMCA-SAM achieves better performance than existing semantic segmentation models and SOTA foundation models. Analysis also shows that topographic constraints lead to a significant improvement in landslide spatial localization accuracy. This method, aiming to obtain accurate boundary geometry, provides reliable spatial assistance for accurate earthwork estimation and damage assessment after a disaster.