[PERSON]  
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China; [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (L.T. ); [EMAIL_ADDRESS] (S.Y.) [PERSON]  
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China; [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (L.T. ); [EMAIL_ADDRESS] (S.Y.) [PERSON]  
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China; [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (L.T. ); [EMAIL_ADDRESS] (S.Y.) [PERSON]  
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China; [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (Z.Y. ); [EMAIL_ADDRESS] (L.T. ); [EMAIL_ADDRESS] (S.Y.) [PERSON]  
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China; lijancun@mail.cgs. GIS-assisted models [7; 8], and analytic hierarchy processes [9; 10]. These models have played certain roles in different scenarios, but the existing models have also revealed some shortcomings in practical applications. For example, the logistic regression model exhibits limited adaptability in complex geological environments and struggles to accurately capture the subtle changes associated with geological disaster occurrences. GIS-assisted models frequently fail to fully account for the dynamic changes and interactions among multiple factors, leading to a lack of timeliness and precision in the evaluation outcomes. Moreover, challenges in data acquisition and processing represent a significant constraint on the application of these models. Therefore, it is particularly urgent to explore more efficient and accurate evaluation methods to overcome the limitations of existing models. InSAR technology not only provides large-scale, high-precision, and high spatiotemporal resolution surface deformation information but also has strong continuity, making it widely applied in geological hazard monitoring and assessment [11; 12]. Currently, some research results have been achieved by integrating time-series InSAR deformation information as an evaluation factor in geological disaster risk assessment [13; 14]. For instance, several studies have shown that the combination of ascending or descending InSAR deformation to evaluate geological hazard susceptibility assessment has significant advantages. One study replaced traditional geological hazard points with ascending time-series InSAR deformation points and integrated deformation magnitude into the evaluation model for assessing geological hazard susceptibility in Baiyin City, confirming the feasibility and effectiveness of this approach [15]. In addition, another study combined dynamic factors derived from InSAR deformation decomposition with 14 static factors to construct a landslide susceptibility model for Lanzhou City. The results showed that the dynamic factors could significantly improve the accuracy of the model [16]. A further study explored landslide identification using InSAR data, incorporating deformation results as evaluation factors in the susceptibility model, highlighting the potential application of this method in susceptibility assessment [17]. Additionally, the integration of machine learning and InSAR technology for landslide hazard assessment provides a novel approach for related research [18]. InSAR deformation data has also been used to update geological hazard susceptibility zoning maps for river basins and slopes, improving the accuracy of disaster prediction [19; 20; 21]. Based on the above, it is evident that integrating InSAR deformation results into assessment models has emerged as a new research focus.