ResM-FusionNet for efficient landslide detection algorithm with a hybrid architecture

Abstract

Landslides, as a prevalent geological hazard, pose a severe threat to both the environment and human society. The rapid and accurate identification of landslide-prone areas is crucial for disaster response, risk assessment, and urban planning. This paper proposes a novel deep learning-based landslide detection method ResM-FusionNet, which leverages ResNet-50 as the backbone for feature extraction and integrates a multi-layer perceptron as the decoder to enhance segmentation accuracy. To address the challenges of complex terrain and boundary detail detection in landslide-prone regions, we introduce a novel loss function, RLoss, which integrates masking mechanisms, semantic weighting, and a Top-K pixel loss averaging strategy. Experimental results on remote sensing datasets demonstrate that ResM-FusionNet significantly outperforms existing models. Specifically, ResM-FusionNet achieves 94.33% accuracy, 85.73% F1-score, and a Kappa coefficient of 70.12%, surpassing other models (e.g., SegFormer, DeepLabv3, and UNet) by 4.4%, 7.7%, and 17.6% in accuracy, respectively. Moreover, ResM-FusionNet excels in boundary detection, achieving an IoU of 0.7545, precision of 85.61%, and recall of 83.92%. These findings indicate that the proposed method not only provides robust and accurate landslide detection but also enhances segmentation performance in complex terrains.