Landslides pose severe threats to life and property, necessitating rapid and accurate identification for effective hazard assessment and emergency response. This study proposes an improved SegFormer model designed for precise landslide extraction from high-resolution remote sensing imagery. To address the limitations of existing segmentation methods in handling complex backgrounds and irregular boundaries, the proposed framework integrates several structural enhancements. These include a squeeze-and-excitation module to suppress background noise, an auxiliary edge-fusion branch to capture explicit boundary details, and an adaptive feature gating mechanism to refine feature representation. The model is trained using focal loss to mitigate class imbalance and employs a three-stage recognition process, culminating in post-processing via dense conditional random fields for boundary refinement. Experimental results on a dataset of 100 high-resolution satellite images demonstrate that this approach significantly outperforms the classical U-Net architecture and traditional thresholding techniques. The model achieved an accuracy of 0.976, a precision of 0.918, a recall of 0.947, and an F1-score of 0.932. These findings confirm that the proposed method offers exceptional accuracy and robustness, providing an effective automated tool for large-scale landslide detection in complex terrain.

Landslides pose severe threats to life and property, necessitating rapid and accurate identification for effective hazard assessment and emergency response. This study proposes an improved SegFormer model for precise landslide extraction from high-resolution remote sensing imagery. To address the limitations of existing segmentation methods in handling complex backgrounds and irregular boundaries, the proposed framework integrates several structural enhancements. These include a squeeze-and-excitation module to suppress background noise, an auxiliary edge-fusion branch to capture explicit boundary details, and an adaptive feature gating mechanism to refine feature representation. The model is trained using focal loss to mitigate class imbalance and employs a three-stage recognition process, culminating in post-processing with dense conditional random fields for boundary refinement. Herein, the experimental results on a dataset of 100 high-resolution satellite images demonstrate that this approach outperforms the classical U-Net architecture and traditional thresholding techniques. The model achieved an accuracy of 0.976, a precision of 0.918, a recall of 0.947, and an F1-score of 0.932. These findings confirm that the proposed method offers exceptional accuracy and robustness, providing an effective automated tool for large-scale landslide detection in complex terrain.