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, The study introduces 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. An adaptive feature enhancement module (AFEM) is introduced to further enhance feature representation by effectively integrating global context with local details, which enhances both multiscale feature richness and boundary clarity. Additionally, an omnidirectional multiscale scanning (OMSS) mechanism is proposed to improve contextual modeling and preserve computational efficiency by integrating omnidirectional attention with multi-scale feature extraction. Comprehensive evaluations on two benchmark datasets show that MSCG-Net outperforms existing methods, achieving IoU scores of 78.04% on the Bijie Landslide Dataset and 81.13% on the global very-high-resolution landslide mapping (GVLM) dataset.