Mask R-CNN–Based Landslide Hazard Identification for 22.6 Extreme Rainfall Induced Landslides in the Beijiang River Basin, China

Abstract

Landslides triggered by extreme precipitation events pose a significant threat to human life and property in mountainous regions. Therefore, accurate identification of landslide locations is crucial for effective prevention and mitigation strategies. During the prolonged heavy rainfall events in Guangdong Province between 21 May and 21 June 2022, shallow and clustered landslides occurred in the mountainous regions of the Beijiang River Basin. This research used high-resolution satellite imagery and integrated the Mask R-CNN algorithm model with spectral, textural, morphological and physical characteristics of landslides in remote sensing imagery, in addition to landslide-influencing factors and other constraints, to interpret the landslides induced by the event through remote sensing techniques. The detection results show that the proposed methodology achieved a high level of accuracy in landslide identification, with a precision rate of 81.91%, a recall rate of 84.07% and an overall accuracy of 87.28%. A total of 3782 shallow landslides were detected, showing a distinct clustered distribution pattern. The performance of Mask R-CNN, Faster-CNN, U-Net and YOLOv3 models in landslide identification was further compared, and the effects of setting the rotation angle and constraints on the identification results of the Mask R-CNN algorithm model were investigated. The results show that each model improves the evaluation indices, but the Mask R-CNN model has the best detection performance; the rotation angle can effectively improve the generalization ability and robustness of the model, and the landslide-inducing factor data and texture feature sample data are the best for landslide identification. The research results provide valuable references and technical support for deepening our understanding of the distribution patterns of rainfall-triggered shallow and cluster landslides in the Beijiang River Basin.

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

“…Yang et al [87] addressed the problem of erroneous extraction due to confusing features, added landslide triggering factors as auxiliary information in the input image data to achieve the purpose of background enhancement, and verified the feasibility of the method using the Mask R-CNN model: the accuracy was 88.68%. Wu et al [88] combined the Mask R-CNN model with the deciphered markers of landslides in remote sensing imagery for landslide identification, and the results showed that the overall accuracy of the method for landslide identification was close to 90%.…”

Active Deformation Areas of Potential Landslide Mapping with a Generalized Convolutional Neural Network

“…Yu et al [40] developed a hierarchical-attention multi-scale deconvolution network and yielded 21% higher F1 scores for landslide identification compared with six networks (Unet, SegNet, DeepLabv3, PSPNet, BiSeNet). Wu et al [43] employed the mask R-CNN model and combined spectral, textural, morphological, and physical characteristics to extract rainfall-induced landslides in Beijiang River Basin with a precision rate of 81.91%, a recall rate of 84.07%, and an overall accuracy of 87.28%. Fu et al [44] proposed the YOLOv3 network and InSAR phase-gradient stacking maps to extract 3366 slow-moving landslides in southwestern China.…”