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  2. 地质与地球物理研究所
  3. 中国科学院地质与地球物理研究所
  4. 中国科学院页岩气与地质工程重点实验室
Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, China

文献类型：期刊论文

作者Ma, Shiwei2,5,6,7; Li, Shouding2,5,6; Bi, Xintao3; Qiao, Hua4; Duan, Zhigang1; Sun, Yiming2,5,6; Guo, Jingyun2,5,6; Li, Xiao2,5,6
刊名FRONTIERS IN EARTH SCIENCE
出版日期2023-09-22
卷号11页码:13
关键词geological disaster landslide identification GoogLeNet model Dual Graph Convolutional Network model
DOI10.3389/feart.2023.1248340
英文摘要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.
资助项目This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re[42077440] ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re[41202217] ; National Natural Science Foundation of China[2019QZKK0904] ; National Natural Science Foundation of China[BJH16J030] ; National Natural Science Foundation of China[BJH16J032] ; second Tibet Plateau Scientific Expedition and Research[2018YFC1505503] ; National Key Research and Development Program of China[ZDRW-ZS-2021-3-1] ; National Key Research and Development Program of China[ZDBS-LY-DQC003] ; Key Developing Program of the Chinese Academy of Sciences[2022DJ5503] ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; CAS Key Technology Talent Program
WOS研究方向Geology
语种英语
出版者FRONTIERS MEDIA SA
WOS记录号WOS:001078872700001
资助机构This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; second Tibet Plateau Scientific Expedition and Research ; second Tibet Plateau Scientific Expedition and Research ; second Tibet Plateau Scientific Expedition and Research ; second Tibet Plateau Scientific Expedition and Research ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Key Developing Program of the Chinese Academy of Sciences ; Key Developing Program of the Chinese Academy of Sciences ; Key Developing Program of the Chinese Academy of Sciences ; Key Developing Program of the Chinese Academy of Sciences ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; CAS Key Technology Talent Program ; CAS Key Technology Talent Program ; CAS Key Technology Talent Program ; CAS Key Technology Talent Program ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; second Tibet Plateau Scientific Expedition and Research ; second Tibet Plateau Scientific Expedition and Research ; second Tibet Plateau Scientific Expedition and Research ; second Tibet Plateau Scientific Expedition and Research ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Key Developing Program of the Chinese Academy of Sciences ; Key Developing Program of the Chinese Academy of Sciences ; Key Developing Program of the Chinese Academy of Sciences ; Key Developing Program of the Chinese Academy of Sciences ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; CAS Key Technology Talent Program ; CAS Key Technology Talent Program ; CAS Key Technology Talent Program ; CAS Key Technology Talent Program ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; second Tibet Plateau Scientific Expedition and Research ; second Tibet Plateau Scientific Expedition and Research ; second Tibet Plateau Scientific Expedition and Research ; second Tibet Plateau Scientific Expedition and Research ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Key Developing Program of the Chinese Academy of Sciences ; Key Developing Program of the Chinese Academy of Sciences ; Key Developing Program of the Chinese Academy of Sciences ; Key Developing Program of the Chinese Academy of Sciences ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; CAS Key Technology Talent Program ; CAS Key Technology Talent Program ; CAS Key Technology Talent Program ; CAS Key Technology Talent Program ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; This research was financially supported by the National Natural Science Foundation of China (42077440, 41202217), the second Tibet Plateau Scientific Expedition and Research (2019QZKK0904), the project (Grant Nos. BJH16J030, BJH16J032), the National Key Re ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; second Tibet Plateau Scientific Expedition and Research ; second Tibet Plateau Scientific Expedition and Research ; second Tibet Plateau Scientific Expedition and Research ; second Tibet Plateau Scientific Expedition and Research ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Key Developing Program of the Chinese Academy of Sciences ; Key Developing Program of the Chinese Academy of Sciences ; Key Developing Program of the Chinese Academy of Sciences ; Key Developing Program of the Chinese Academy of Sciences ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; Scientific Research and Technology Development Project of China National Petroleum Corporation ; CAS Key Technology Talent Program ; CAS Key Technology Talent Program ; CAS Key Technology Talent Program ; CAS Key Technology Talent Program
源URL[http://ir.iggcas.ac.cn/handle/132A11/110758]  
专题地质与地球物理研究所_中国科学院页岩气与地质工程重点实验室
通讯作者Li, Shouding
作者单位1.Naval Res Acad, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China
3.Shengli Oil Field Explorat & Dev Res Inst, Dongying, Shandong, Peoples R China
4.Geol Environm Monitoring Inst Xinjiang Uygur Auton, Urumqi, Xinjiang, Peoples R China
5.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing, Peoples R China
6.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Shale Gas & Geoengn, Beijing, Peoples R China
7.China Inst Geoenvironm Monitoring, Tech Guidance Ctr Geohazards Prevent MNR, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Ma, Shiwei,Li, Shouding,Bi, Xintao,et al. Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, China[J]. FRONTIERS IN EARTH SCIENCE,2023,11:13.
APA Ma, Shiwei.,Li, Shouding.,Bi, Xintao.,Qiao, Hua.,Duan, Zhigang.,...&Li, Xiao.(2023).Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, China.FRONTIERS IN EARTH SCIENCE,11,13.
MLA Ma, Shiwei,et al."Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, China".FRONTIERS IN EARTH SCIENCE 11(2023):13.

入库方式： OAI收割

来源：地质与地球物理研究所

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