[24] used high-resolution imagery and increased track shape variables to identify landslides in Dongchuan District, such that they obtained geohazard data for the area. [PERSON] et al. [25] identified landslides in the Alpine Valley area by employing five InSAR methods. Their results indicated that SBAS-InSAR technology can effectively identify landslides in the Alpine Valley area. [PERSON] et al. [26] proposed an optimized InSAR-based procedure to map large landslides in loess hills regions, which detects and maps a total of 50 potential loess landslides based on tropospheric delay correction through quadtree segmentation and automatic selection of interferograms based on minimum error boundaries. The above research suggested that the emergence of InSAR technology significantly increases the uncertainty of landslides, which can effectively help the government detect, recognize, predict, and prevent landslides in time while revealing an extremely important role of surface deformation in landslide monitoring. Furthermore, it lays a scientific basis for the experiments in this study. Landslide susceptibility is mainly derived by using the following methods. Based on geo-environmental factors of landslide occurrence, the degree of landslide influence on the evaluation factors is statistically analyzed, the relative probability value of landslide occurrence in each unit is calculated through the model, and the degree of susceptibility to the region is classified. The landslide susceptibility model primarily includes two categories: statistical model and machine learning. Statistical models comprise an information quantity model (I), logistic regression (LR), multiple linear regression (MLR), weight of evidence (WOE), index of entropy (IOE), frequency ratio (FR), certainty factor (CF), and other models [27; 28; 29; 30; 31]. Machine learning consists of models such as random forest (RF), support vector machine (SVM), artificial neural network (ANN), convolutional neural network (CNN), deep learning, and so forth [32; 33; 34; 35; 36; 37; 38]. With the development of computers, landslide susceptibility based on machine learning has been extensively employed. For instance, [PERSON] et al. [39] adopted random forest to predict landslide susceptibility probability maps. Their results indicated that random forests possess high performance in modeling landslide susceptibility. [PERSON] et al. [40] adopted 217 landslide datasets from the Muong Lay area in Vietnam to evaluate landslide susceptibility using SVM, ANN, and LR. Their result indicated that the SVM model possesses a good prediction accuracy. [PERSON] et al. [41] optimized the hyperparameters of the random forest model by using the Bayesian algorithm and then selected the optimal hyperparameters for landslide susceptibility. Specifically, the method improves the model's accuracy by 4%. [PERSON] et al. [42] compared the prediction performance of LR, 5-CV-SVM, GA-SVM, and PSO-SVM for landslide susceptibility in the Tibetan Plateau region, and the result indicated that PSO-SVM exhibits better performance in landslide susceptibility evaluation. The above machine learning has achieved certain results, whereas the phenomenon of insufficient accuracy and model overfitting may occur under the complex network structure and considerable sample data. Accordingly, some scholars have combined convolutional neural network and GIS landslide susceptibility research to reduce the overfitting phenomenon of the model by virtue of its powerful feature extraction ability and linear regression fitting ability and they have further increased the prediction accuracy of the landslide susceptibility model. Based on the landslides in Wanzhou District, Chongqing, China, which is located in the Three Gorges Reservoir area, [PERSON] et al. [43] evaluated landslide susceptibility by using the oversampling technique combined with CNN, and the accuracy of the model reached up to 89.50%.