Extensive identification of landslide boundaries using remote sensing images and deep learning method  

作　　者：Chang-dong Li Peng-fei Feng Xi-hui Jiang Shuang Zhang Jie Meng Bing-chen Li 

机构地区：[1]Faculty of Engineering,China University of Geoscience,Wuhan 430074,China [2]Badong National Observation and Research Station of Geohazards,China University of Geosciences,Wuhan 430074,China [3]School of Mechanical Engineering and Electronic Information,China University of Geosciences,Wuhan 430074,China [4]College of Geology Engineering and Geomatics,Chang’an University,Xi’an 710054,China

出　　处：《China Geology》2024年第2期277-290,共14页中国地质（英文）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.42090054,41931295);the Natural Science Foundation of Hubei Province of China(2022CFA002)。

摘　　要：The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

关 键 词：GEOHAZARD Landslide boundary detection Remote sensing image Deep learning model Steep slope Large annual rainfall Human settlements INFRASTRUCTURE Agricultural land Eastern Tibetan Plateau Geological hazards survey engineering 

分 类 号：P642.22[天文地球—工程地质学] P237[天文地球—地质矿产勘探]