Landslides are the most serious geological disaster in our country, causing economic losses. Because they go undetected, a large number of landslides that have caused disasters are not in the catalogue. At present, Interferometric Synthetic Aperture Radar (InSAR) has been widely used in the identification of landslides. However, it is time-consuming, inefficient, etc., to survey landslides throughout our large country. In the context of massive SAR data, this problem is more obvious. Therefore, based on the current technique of using differential interferogram phase gradient stacking to avoid phase unwrapping errors, a landslide phase gradient dataset has been constructed. To validate the dataset’s effectiveness and applicability, deep learning methods were introduced, applying the dataset to four networks: U-Net, Attention-Unet, Bisenet v2, and Deeplab v3. The results indicate that the phase gradient dataset performs well across different models, with the Attention-Unet network demonstrating the best performance. Specifically, the precision, recall, and accuracy on the test dataset were 0.8771, 0.8712, and 0.9834, respectively, and the accuracy on the validation dataset was 0.8523. Finally, in this paper, the model is applied to landslide identification in Gansu Province, China, during 2022-2023, and a total of 1882 landslides are found. These landslides are mainly concentrated in the south of Gansu Province, where the terrain is relatively undulating. The results show that this method can quickly and accurately realize landslide automatic identification in a wide area and provide technical support for large-scale landslide disaster surveys.

Under rapid global climate change, the risk of ancient landslide reactivation induced by rainfall infiltration is increasing significantly. The contribution of cracks to the reactivation of ancient landslides, as an evolutionary product, is a topic that deserves attention; however, current research on this issue remains insufficient. In this study, taking the Woda landslide in the upper Jinsha River as a case study, we investigated the reactivation mechanisms of ancient landslides with and without cracks under rainfall based on model tests. The study showed that cracks influence the reactivation range and depth of ancient landslide. In cases where no cracks develop on ancient landslides, rainfall can only cause shallow sliding with failure concentrated at its front edge. Conversely, when cracks develop on ancient landslides, rainwater can quickly infiltrate into the sliding zone along the cracks and induce overall reactivation of the ancient landslide. Furthermore, the reactivation mechanism of ancient landslides without cracks is that the failure of ancient landslide foot results in progressive failure at the front of the ancient landslide. When cracks have developed at ancient landslides, the reactivation mechanism of which involves mid-rear ancient landslide creeping, tensile cracks develop on the mid-rear ancient landslide, with localized sliding at the front edge, tensile cracks extending, local sliding range extending, accelerated creeping, and progressive failure of the mid-rear ancient landslide. These findings shed light on how cracks influence rainfall-induced mechanisms of ancient landslide reactivation and hold great significance for advancing our understanding regarding these mechanisms.