NestedMambaUNet: A Direction-Aware State Space Network for Landslide Mapping from Remote Sensing Images

The rapid and accurate extraction of landslide areas from remote sensing imagery is critical for post-disaster emergency response and rescue operations. However, landslides exhibit complex morphologies and irregular orientations and are easily confused with background features such as bare ground and roads. However, accurately modeling long-range spatial dependencies, effective multi-scale feature fusion, and precise boundary delineation for complex landslide scenarios remains challenging. To address these challenges, we propose NestedMambaUNet, a nested state-space network specifically designed for landslide extraction in remote sensing imagery. Building upon the dense skip-connection architecture of UNet++, the model first introduces a coordinate-enhanced convolution module called CoordConvBlock to explicitly encode spatial positional information during shallow feature extraction, thereby improving the modeling of spatial relationships between landslides and surrounding terrain. It further incorporates a 2D direction-adaptive selective scanning mechanism (DASS2D), which adaptively aggregates scanning results from four directions (horizontal, vertical, main diagonal, and anti-diagonal) to capture long-range spatial dependencies aligned with the irregular structures of landslides. Additionally, a direction-adaptive selective scanning fusion block (DASS Fusion Block) is designed to enhance multi-scale feature integration and improve boundary continuity by combining attention-based gating for skip connections with direction-adaptive state-space modeling. The experimental results on two public datasets, LMHLD and HR-GLDD, demonstrate that the proposed method outperforms competing approaches across multiple evaluation metrics. Specifically, the IoU reaches 71.23% and 58.84%, representing improvements of 1.17 and 5.65 percentage points over the second-best method, respectively, while the recall increases by 4.05 and 12.01 percentage points, respectively. Although the proposed method exhibits a slight reduction in Precision on certain datasets, it achieves the best overall F1-score, indicating a favorable balance between missed detection reduction and false positive control for landslide extraction tasks. These results indicate that NestedMambaUNet effectively improves the structural integrity of landslides and enhances boundary delineation, while exhibiting good robustness across different data distributions and geographic scenarios. In addition, the proposed method achieves a favorable balance between segmentation accuracy and computational efficiency, demonstrating its potential for time-sensitive large-scale landslide mapping applications.