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

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

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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 0.8 and 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 investigations and applications in related domains.

GeohazardLandslide boundary detectionRemote sensing imageDeep learning modelSteep slopeLarge annual rainfallHuman settlementsInfrastructureAgricultural landEastern Tibetan PlateauGeological hazards survey engineering

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

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Faculty of Engineering,China University of Geoscience,Wuhan 430074,China

Badong National Observation and Research Station of Geohazards,China University of Geosciences,Wuhan 430074,China

School of Mechanical Engineering and Electronic Information,China University of Geosciences,Wuhan 430074,China

College of Geology Engineering and Geomatics,Chang'an University,Xi'an 710054,China

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of Hubei Province of China

Grant Nos.42090054419312952022CFA002

2024

10.31035/cg2023148

中国地质(英文)
中国地质调查局,中国地质科学院

中国地质(英文)

CSTPCD
ISSN：2096-5192
年,卷(期)：2024.7(2)
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