Landslide Detection from Open Satellite Imagery Using Distant Domain Transfer Learning

Shengwu Qin,Jingbo Sun,Yanqing Zhang,Xu Guo,Shuangshuang Qiao,Lingshuai Zhang,Jingyu Yao,Qiushi Cheng

doi:10.3390/rs13173383

Abstract

Using convolutional neural network (CNN) methods and satellite images for landslide identification and classification is a very efficient and popular task in geological hazard investigations. However, traditional CNNs have two disadvantages: (1) insufficient training images from the study area and (2) uneven distribution of the training set and validation set. In this paper, we introduced distant domain transfer learning (DDTL) methods for landslide detection and classification. We first introduce scene classification satellite imagery into the landslide detection task. In addition, in order to more effectively extract information from satellite images, we innovatively add an attention mechanism to DDTL (AM-DDTL). In this paper, the Longgang study area, a district in Shenzhen City, Guangdong Province, has only 177 samples as the landslide target domain. We examine the effect of DDTL by comparing three methods: the convolutional CNN, pretrained model and DDTL. We compare different attention mechanisms based on the DDTL. The experimental results show that the DDTL method has better detection performance than the normal CNN, and the AM-DDTL models achieve 94% classification accuracy, which is 7% higher than the conventional DDTL method. The requirements for the detection and classification of potential landslides at different disaster zones can be met by applying the AM-DDTL algorithm, which outperforms traditional CNN methods.

Highlights

With the rapid development of cities, urban construction sites have expanded to low hills, with engineering construction occurring on many unstable slopes [1]
We present an overview of the different improved convolutional block attention module (CBAM) distant domain transfer learning (DDTL) model
The bottom of the convolutional neural tion task, but the convolutional neural network (CNN) model only achieved 86.16% classification accuracy because the network is connected with the flattened layer and the dense layers

Summary

Introduction

With the rapid development of cities, urban construction sites have expanded to low hills, with engineering construction occurring on many unstable slopes [1] Under extreme cases, such as seismic shaking and heavy rainfall, these unstable slopes can become landslides and cause severe damage to the natural environment, property and personal safety [2,3]. Specialists judge whether a landslide has occurred according to optical images, digital elevation model (DEM) data and other geological information [8]. This method is time-consuming and its interpretation accuracy may be poor [8,9]. A novel method that can automatically recognize landslides must be constructed based on new technologies and new methods [10]

Results

Discussion

Conclusion