A Multi-Scale Feature Fusion Dual-Branch Mamba-CNN Network for Landslide Extraction

Automatically extracting landslide regions from remote sensing images plays a vital role in the landslide inventory compilation. However, this task remains challenging due to the considerable diversity of landslides in terms of morphology, triggering mechanisms, and internal structure. Thanks to its efficient long-sequence modeling, Mamba has emerged as a promising candidate for semantic segmentation tasks. This study adopts Mamba for landslide extraction to improve the recognition of complex geomorphic features. While Mamba demonstrates strong performance, it still faces challenges in capturing spatial dependencies and preserving fine-grained local information. To address these challenges, we propose a multi-scale spatial context-guided network (MSCG-Net). MSCG-Net features a dual-branch architecture, comprising a convolutional neural network (CNN) branch that captures detailed spatial features and an omnidirectional multi-scale Mamba (OMM) branch that models long-range contextual dependencies. We introduce an adaptive feature enhancement module (AFEM) to further enhance feature representation by effectively integrating global context with local details, which enhances both multiscale feature richness and boundary clarity. Additionally, we develop an omnidirectional multiscale scanning (OMSS) mechanism to improve contextual modeling and preserve computational efficiency by integrating omnidirectional attention with multi-scale feature extraction. Comprehensive evaluations on two benchmark datasets demonstrate that MSCG-Net outperforms existing approaches, achieving IoU scores of 78.04% on the Bijie dataset and 81.13% on the GVLM dataset. Furthermore, it exceeds the second-best methods by 2.28% and 4.25% in Boundary IoU, respectively.