Potential Landslide Identification Based on Improved YOLOv8 and InSAR Phase-Gradient Stacking

Landslides, as a major geological hazard, caused the significant threats to human life and property. Therefore, the identification of potential landslides is a crucial concern. This study combines synthetic aperture radar interferometry phase-gradient stacking with deep learning to achieve efficient and accurate identification of large-scale potential landslides. By stacking phase gradients to highlight local surface deformation information and refining the YOLOv8 model based on small target features of local deformations, this study introduces improvements. This involves adding a convolutional block attention module layer to the backbone, replacing the C2f modules with GhostNetV2 to suppress information loss during long-distance feature transmission, and enhancing the network's perception and detection capabilities for small targets. In addition, a 160 × 160 small target detection head is added to the detection module to specifically handle small target detection tasks, improving accuracy and performance. A new loss function, FocalSIoU loss, is introduced based on the characteristics of the dataset, combining SIoU with the gamma bias option to make the model more targeted during training. The improved model achieved a maximum mAP50 value of 93.4% on the validation set. Finally, using mask factors to identify region deformation points, this study reduces misjudgments of non-landslides, identifying 378 potential landslides in the study area with a false positive rate of only 10.2%.