Combining landslide inventories with deformation time series: A methodology

Landslide Susceptibility Modeling (LSM) is an important method for mitigating regional landslide risks. However, the scarcity of landslide inventories and the prevalence of low-quality non-landslide samples significantly limit the further development of traditional LSM frameworks. To address this issue, this paper develops a next generation of LSM framework that redefines landslide and non-landslide samples from the perspective of deformation. By integrating deformation time-series data from Global Navigation Satellite System (GNSS) and InSAR, the framework introduces deformation samples defined by deformation rates and obtains a greater number of landslide samples and high-quality non-landslide samples through the establishment of appropriate deformation thresholds. A series of ablation experiments were conducted in Wanzhou District, Chongqing, China. The results indicate that when the deformation threshold is set to 0.6, the proposed LSM framework achieves an AUC value of 0.94, a TPR of 0.92, and a TNR of 0.94, representing a significant improvement compared to the traditional LSM framework (AUC = 0.85, TPR = 0.74, TNR = 0.58). Additionally, the experimental results demonstrate that when using InSAR data to obtain deformation samples, either a large number of low-quality InSAR deformation samples or a small number of high-quality but spatially uneven InSAR deformation samples can result in the proposed LSM framework performing worse than the traditional LSM framework. Therefore, special attention must be paid to balancing the quality and quantity of InSAR data.