Automatic Extraction of Seismic Landslides in Large Areas with Complex Environments Based on Deep Learning: An Example of the 2018 Iburi Earthquake, Japan

Pengfei Zhang,Yingying Tian,Boyu Wen,Siyuan Ma,Chong Xu,Xiaoyi Shao

doi:10.3390/rs12233992

Abstract

After a major earthquake, the rapid identification and mapping of co-seismic landslides in the whole affected area is of great significance for emergency rescue and loss assessment of seismic hazards. In recent years, researchers have achieved good results in research on a small scale and single environment characteristics of this issue. However, for the whole earthquake-affected area with large scale and complex environments, the correct rate of extracting co-seismic landslides remains low, and there is no ideal method to solve this problem. In this paper, Planet Satellite images with a spatial resolution of 3 m are used to train a seismic landslide recognition model based on the deep learning method to carry out rapid and automatic extraction of landslides triggered by the 2018 Iburi earthquake, Japan. The study area is about 671.87 km2, of which 60% is used to train the model, and the remaining 40% is used to verify the accuracy of the model. The results show that most of the co-seismic landslides can be identified by this method. In this experiment, the verification precision of the model is 0.7965 and the F1 score is 0.8288. This method can intelligently identify and map landslides triggered by earthquakes from Planet images. It has strong practicability and high accuracy. It can provide assistance for earthquake emergency rescue and rapid disaster assessment.

Highlights

Co-seismic landslides are a major secondary effect of earthquakes, of which the loss usually accounts for a large proportion of the total loss of an earthquake disaster [1,2]
It is of great significance to obtain information on the location, scope and size of co-seismic landslides quickly and accurately to guide earthquake emergency rescue, disaster assessment and reconstruction [3]
ENVI deep learning relies on the TensorFlow model to perform classification, which is the core of the whole process

Summary

Introduction

Co-seismic landslides are a major secondary effect of earthquakes, of which the loss usually accounts for a large proportion of the total loss of an earthquake disaster [1,2]. Co-seismic landslides can cause road damage, river blockage, house burial and bridge collapse, making emergency rescue and on-site investigation difficult. This will seriously affect life rescue and earthquake disaster assessment. Most of their work focused on small-scale issues and in a single environment. They have achieved good results [4,5,6]. For the whole earthquake-affected area with a large scale and complex environment, the extraction accuracy of co-seismic landslides is low, and there is no ideal method to solve this problem [7,8]

Objectives

Methods

Results

Conclusion