An Overview of Landslide Detection: Deep Learning and Machine Learning Approaches

Abstract

Landslide is a devastating natural disaster with the frequent occurrence and tremendous destructive power. Once it happened, human society, the safety of life and property, and the natural environment would suffer enormous losses. The purpose of landslide research is to reduce landslide occurrence probability through manual intervention to some extent, in which landslide detection is one of the fundamental researches in this field. For state-of-art studies, the hotspot of landslide detection primarily focuses on Deep Learning (DL) and Machine Learning (ML) approaches. In this paper, we summarize the primary works in the field of landslide research firstly. Then the acquisition and usage of landslide data for DL and ML approaches are introduced. Next, the most frequently used evaluation indexes of object detection and image segmentation. Finally, the relevant progress of DL and ML approaches in landslide detection research are reviewed. Meanwhile, the challenges and future research directions in this field are further discussed.