Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, China
Landslides are a natural disaster that exists widely in the world and poses a great threat to human life and property, so it is of great importance to identify and locate landslides. Traditional manual interpretation can effectively identify landslides, but its efficiency is very low for large inter...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2023.1248340/full |
| _version_ | 1827809116468281344 |
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| author | Shiwei Ma Shiwei Ma Shiwei Ma Shiwei Ma Shouding Li Shouding Li Shouding Li Xintao Bi Hua Qiao Zhigang Duan Yiming Sun Yiming Sun Yiming Sun Jingyun Guo Jingyun Guo Jingyun Guo Xiao Li Xiao Li Xiao Li |
| author_facet | Shiwei Ma Shiwei Ma Shiwei Ma Shiwei Ma Shouding Li Shouding Li Shouding Li Xintao Bi Hua Qiao Zhigang Duan Yiming Sun Yiming Sun Yiming Sun Jingyun Guo Jingyun Guo Jingyun Guo Xiao Li Xiao Li Xiao Li |
| author_sort | Shiwei Ma |
| collection | DOAJ |
| description | Landslides are a natural disaster that exists widely in the world and poses a great threat to human life and property, so it is of great importance to identify and locate landslides. Traditional manual interpretation can effectively identify landslides, but its efficiency is very low for large interpreted areas. In this sense, a landslide recognition method based on the Dual Graph Convolutional Network (DGCNet) is proposed to identify the landslide in remote sensing images quickly and accurately. The remote sensing image (regional remote sensing image) of the northern mountainous area of Tuergen Township, Xinyuan County, Xinjiang Province, was obtained by GeoEye-1 (spatial resolution: 0.5 m). Then, the DGCNet is used to train the labeled images, which finally shows good accuracy of landslide recognition. To show the difference with the traditional convolutional network model, this paper adopts a convolution neural network algorithm named GoogLeNet for image recognition to carry out a comparative analysis, the remote sensing satellite images (single terrain image) of Xinyuan County, Xinjiang Province is used as the data set, and the prediction accuracy is 81.25%. Compared with the GoogLeNet model, the DGCNet model has a larger identification range, which provides a new method for landslide recognition of large-scale regional remote sensing images, but the performance of DGCNet is highly dependent on the quality and characteristics of the input image. If the input data quality is poor or the image structure is unclear, the model’s performance may decline. |
| first_indexed | 2024-03-11T22:38:28Z |
| format | Article |
| id | doaj.art-07afe8e069de44aaa45347efc2c846d4 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-03-11T22:38:28Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-07afe8e069de44aaa45347efc2c846d42023-09-22T09:24:19ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-09-011110.3389/feart.2023.12483401248340Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, ChinaShiwei Ma0Shiwei Ma1Shiwei Ma2Shiwei Ma3Shouding Li4Shouding Li5Shouding Li6Xintao Bi7Hua Qiao8Zhigang Duan9Yiming Sun10Yiming Sun11Yiming Sun12Jingyun Guo13Jingyun Guo14Jingyun Guo15Xiao Li16Xiao Li17Xiao Li18Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, ChinaInnovation Academy for Earth Science, Chinese Academy of Sciences, Beijing, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, ChinaChina Institute of Geo-Environment Monitoring, Technical Guidance Center for Geo-Hazards Prevention of MNR, Beijing, ChinaKey Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, ChinaInnovation Academy for Earth Science, Chinese Academy of Sciences, Beijing, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, ChinaShengli Oil Field Exploration and Development Research Institute, Shandong, ChinaGeological Environment Monitoring Institute of Xinjiang Uygur Autonomous Region, Urumchi, Xinjiang, ChinaNaval Research Academy, Beijing, ChinaKey Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, ChinaInnovation Academy for Earth Science, Chinese Academy of Sciences, Beijing, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, ChinaInnovation Academy for Earth Science, Chinese Academy of Sciences, Beijing, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, ChinaInnovation Academy for Earth Science, Chinese Academy of Sciences, Beijing, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, ChinaLandslides are a natural disaster that exists widely in the world and poses a great threat to human life and property, so it is of great importance to identify and locate landslides. Traditional manual interpretation can effectively identify landslides, but its efficiency is very low for large interpreted areas. In this sense, a landslide recognition method based on the Dual Graph Convolutional Network (DGCNet) is proposed to identify the landslide in remote sensing images quickly and accurately. The remote sensing image (regional remote sensing image) of the northern mountainous area of Tuergen Township, Xinyuan County, Xinjiang Province, was obtained by GeoEye-1 (spatial resolution: 0.5 m). Then, the DGCNet is used to train the labeled images, which finally shows good accuracy of landslide recognition. To show the difference with the traditional convolutional network model, this paper adopts a convolution neural network algorithm named GoogLeNet for image recognition to carry out a comparative analysis, the remote sensing satellite images (single terrain image) of Xinyuan County, Xinjiang Province is used as the data set, and the prediction accuracy is 81.25%. Compared with the GoogLeNet model, the DGCNet model has a larger identification range, which provides a new method for landslide recognition of large-scale regional remote sensing images, but the performance of DGCNet is highly dependent on the quality and characteristics of the input image. If the input data quality is poor or the image structure is unclear, the model’s performance may decline.https://www.frontiersin.org/articles/10.3389/feart.2023.1248340/fullgeological disasterlandslide identificationGoogLeNet modelDual Graph Convolutional Networkmodel |
| spellingShingle | Shiwei Ma Shiwei Ma Shiwei Ma Shiwei Ma Shouding Li Shouding Li Shouding Li Xintao Bi Hua Qiao Zhigang Duan Yiming Sun Yiming Sun Yiming Sun Jingyun Guo Jingyun Guo Jingyun Guo Xiao Li Xiao Li Xiao Li Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, China Frontiers in Earth Science geological disaster landslide identification GoogLeNet model Dual Graph Convolutional Network model |
| title | Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, China |
| title_full | Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, China |
| title_fullStr | Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, China |
| title_full_unstemmed | Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, China |
| title_short | Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, China |
| title_sort | automatic landslide identification by dual graph convolutional network and googlenet model a case study for xinjiang province china |
| topic | geological disaster landslide identification GoogLeNet model Dual Graph Convolutional Network model |
| url | https://www.frontiersin.org/articles/10.3389/feart.2023.1248340/full |
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