Application of an Incomplete Landslide Inventory and One Class Classifier to Earthquake-Induced Landslide Susceptibility Mapping

Abstract

Rapid and effective evaluation of landslide susceptibility after earthquakes is critical for various applications, such as emergency rescue, land planning, and disaster prevention. Current research suffers from the lack of a complete landslide inventory and sample selection uncertainty issues. To solve these problems, this study presents a landslide susceptibility mapping model that integrates one-class support vector machine (OCSVM) and an incomplete landslide inventory, which was established with the aid of change detection from bi-temporal Landsat images. Wenchuan County is selected as the study area to test the performance of the proposed method. The proposed method is also compared with standard two-class SVM that selects a sample randomly. Experimental results show that OCSVM can achieve better performance than SVM when only an incomplete landslide inventory is available. The findings of this study can be applied to determine regional landslide susceptibility after earthquakes and provide an essential reference for emergency response.