Extensive identification of landslide boundaries using remote sensing images and deep learning method
ABSTRACT: 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 neu...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-04-01
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| Series: | China Geology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096519224001095 |
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| author | Chang-dong Li Peng-fei Feng Xi-hui Jiang Shuang Zhang Jie Meng Bing-chen Li |
| author_facet | Chang-dong Li Peng-fei Feng Xi-hui Jiang Shuang Zhang Jie Meng Bing-chen Li |
| author_sort | Chang-dong Li |
| collection | DOAJ |
| description | ABSTRACT: 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. |
| format | Article |
| id | doaj-art-4aabebc92abc4ea58be8f12666d4eaa4 |
| institution | Kabale University |
| issn | 2589-9430 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | China Geology |
| spelling | doaj-art-4aabebc92abc4ea58be8f12666d4eaa42025-08-20T03:29:31ZengKeAi Communications Co., Ltd.China Geology2589-94302024-04-017227729010.31035/cg2023148Extensive identification of landslide boundaries using remote sensing images and deep learning methodChang-dong Li0Peng-fei Feng1Xi-hui Jiang2Shuang Zhang3Jie Meng4Bing-chen Li5Faculty of Engineering, China University of Geoscience, Wuhan 430074, China; Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China; Corresponding author:School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geoscience, Wuhan 430074, ChinaCollege of Geology Engineering and Geomatics, Chang'an University, Xi'an 710054, ChinaFaculty of Engineering, China University of Geoscience, Wuhan 430074, ChinaFaculty of Engineering, China University of Geoscience, Wuhan 430074, ChinaABSTRACT: 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.http://www.sciencedirect.com/science/article/pii/S2096519224001095GeohazardLandslide boundary detectionRemote sensing imageDeep learning modelSteep slopeLarge annual rainfall |
| spellingShingle | Chang-dong Li Peng-fei Feng Xi-hui Jiang Shuang Zhang Jie Meng Bing-chen Li Extensive identification of landslide boundaries using remote sensing images and deep learning method China Geology Geohazard Landslide boundary detection Remote sensing image Deep learning model Steep slope Large annual rainfall |
| title | Extensive identification of landslide boundaries using remote sensing images and deep learning method |
| title_full | Extensive identification of landslide boundaries using remote sensing images and deep learning method |
| title_fullStr | Extensive identification of landslide boundaries using remote sensing images and deep learning method |
| title_full_unstemmed | Extensive identification of landslide boundaries using remote sensing images and deep learning method |
| title_short | Extensive identification of landslide boundaries using remote sensing images and deep learning method |
| title_sort | extensive identification of landslide boundaries using remote sensing images and deep learning method |
| topic | Geohazard Landslide boundary detection Remote sensing image Deep learning model Steep slope Large annual rainfall |
| url | http://www.sciencedirect.com/science/article/pii/S2096519224001095 |
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