An Automatic Detection Method of Slow-Moving Landslides Using an Improved Faster R-CNN Model Based on InSAR Deformation Rates

Landslides constitute major geohazards that threaten human life, property, and ecological environments; it is imperative to acquire their location information accurately and in a timely manner. Interferometric Synthetic Aperture Radar (InSAR) has been demonstrated to be capable of acquiring subtle surface deformation with high precision and is widely applied to wide-area landslide detection. However, after obtaining InSAR deformation rates, visual interpretation is conventionally employed in landslide detection, which is characterized by significant temporal consumption and labor-intensive demands. Despite advancements that have been made through cluster analysis, hotspot analysis, and deep learning, persistent challenges such as low intelligence levels and weak generalization capabilities remain unresolved. In this study, we propose an improved Faster R-CNN model to achieve automatic detection of slow-moving landslides based on InSAR Line of Sight (LOS) annual rates in the upper and middle reaches of the Jinsha River Basin. The model incorporates a ResNet-34 backbone network, Feature Pyramid Network (FPN), and Convolutional Block Attention Module (CBAM) to effectively extract multi-scale features and enhance focus on subtle surface deformation regions. This model achieved test set performance metrics of 93.56% precision, 97.15% recall, and 93.6% F1-score. The proposed model demonstrates robust detection performance for slow-moving landslides, and through comparative analysis with the detection results of hotspot analysis and K-means clustering, it is verified that this method has strong generalization ability in the representative landslide-prone areas of the Qinghai–Tibet Plateau. This approach can support dynamic updates of regional slow-moving landslide inventories, providing crucial technical support for the detection of landslides.