Active Deformation Areas of Potential Landslide Mapping with a Generalized Convolutional Neural Network

Abstract

Early discovery and monitoring of the active deformation areas of potential landslides are important for geohazard risk prevention. The objective of the study is to propose a one-step strategy for automatically mapping the active deformation areas of potential landslides from a Sentinel-1 SAR dataset. First, we built a generalized convolutional neural network (CNN) based on activity and topographic characteristics. Second, we conducted a comparative analysis of the performance of various multi-channel combiners for detecting the active deformation areas of the potential landslides. Third, we verified the transferability of the pretrained CNN model for an unknown region. We found that by incorporating topographic characteristics into a generalized convolutional neural network, we were able to enhance the accuracy of identifying the active deformation areas of potential landslides, rapidly mapping these areas. The methodology is robust and efficient, and it has the capability to automatically detect the active deformation areas of potential landslides, even in unknown or unfamiliar regions. This product can facilitate automated pipelines, updating and mapping active deformation areas for final users who are not InSAR experts. This implementation can be used for providing support to risk management activities.