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The development process of landslides often has time series characteristics such as surface deformation. However, most of the existing landslide susceptibility assessment (LSA) methods are based on the static characteristics of influencing factors and have not considered the dynamic characteristics of the landslide occurrence time series, resulting in problems such as poor reliability of the evaluation results of landslide susceptibility. Therefore, in this section, the dynamic and static feature integrated LSA method combining InSAR deformation information realizes the comprehensive utilization of dynamic and static features of landslides through the parallel deep learning network model, thereby improving the reliability of LSA.

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An Integrated Dynamic–Static Feature Framework for Landslide Susceptibility Mapping Using InSAR Based Ground Surface Deformation

  • 章节

  • 发表日期：

    2025年

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  • Yi He

摘要

The development process of landslides often has time series characteristics such as surface deformation. However, most of the existing landslide susceptibility assessment (LSA) methods are based on the static characteristics of influencing factors and have not considered the dynamic characteristics of the landslide occurrence time series, resulting in problems such as poor reliability of the evaluation results of landslide susceptibility. Therefore, in this section, the dynamic and static feature integrated LSA method combining InSAR deformation information realizes the comprehensive utilization of dynamic and static features of landslides through the parallel deep learning network model, thereby improving the reliability of LSA.