Loess Landslide Detection Using Object Detection Algorithms in Northwest China
文献类型：期刊论文
| 作者 | Ju, Yuanzhen; Xu, Qiang; Jin, Shichao2; Li, Weile; Su, Yanjun3,4; Dong, Xiujun; Guo, Qinghua5,6
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| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022 |
| 卷号 | 14期号:5 |
| 关键词 | loess landslide google earth image deep learning automatic identification object detection |
| DOI | 10.3390/rs14051182 |
| 文献子类 | Article |
| 英文摘要 | Regional landslide identification is important for the risk management of landslide hazards. The traditional methods of regional landslide identification were mainly conducted by a human being. In previous studies, automatic landslide recognition mainly focused on new landslides distinct from the environment induced by rainfall or earthquake, using the image classification method and semantic segmentation method of deep learning. However, there is a lack of research on the automatic recognition of old loess landslides, which are difficult to distinguish from the environment. Therefore, this study uses the object detection method of deep learning to identify old loess landslides with Google Earth images. At first, a database of loess historical landslide samples was established for deep learning based on Google Earth images. A total of 6111 landslides were interpreted in three landslide areas in Gansu Province, China. Second, three object detection algorithms including the one-stage algorithm RetinaNet and YOLO v3 and the two-stage algorithm Mask R-CNN, were chosen for automatic landslide identification. Mask R-CNN achieved the greatest accuracy, with an AP of 18.9% and F1-score of 55.31%. Among the three landslide areas, the order of identification accuracy from high to low was Site 1, Site 2, and Site 3, with the F1-scores of 62.05%, 61.04% and 50.88%, respectively, which were positively related to their recognition difficulty. The research results proved that the object detection method can be employed for the automatic identification of loess landslides based on Google Earth images. |
| 学科主题 | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| 出版地 | BASEL |
| 电子版国际标准刊号 | 2072-4292 |
| WOS关键词 | EARTHQUAKE ; LIDAR ; IDENTIFICATION ; FOREST |
| WOS研究方向 | Science Citation Index Expanded (SCI-EXPANDED) |
| 语种 | 英语 |
| WOS记录号 | WOS:000768887500001 |
| 出版者 | MDPI |
| 源URL | [http://ir.ibcas.ac.cn/handle/2S10CLM1/28851]  |
| 专题 | 植被与环境变化国家重点实验室 |
| 作者单位 | 1.Peking Univ, Coll Urban & Environm Sci, Inst Ecol, Beijing 100871, Peoples R China 2.Chengdu Univ Technol, State Key Lab Geohazard Prevent & Geoenvironm Pro, Chengdu 610059, Peoples R China 3.Nanjing Agr Univ, Collaborat Innovat Ctr Modern Crop Prod Cosponsor, Acad Adv Interdisciplinary Studies, Plant Phen Res Ctr, Nanjing 210095, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 6.Peking Univ, Sch Earth & Space Sci, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ju, Yuanzhen,Xu, Qiang,Jin, Shichao,et al. Loess Landslide Detection Using Object Detection Algorithms in Northwest China[J]. REMOTE SENSING,2022,14(5). |
| APA | Ju, Yuanzhen.,Xu, Qiang.,Jin, Shichao.,Li, Weile.,Su, Yanjun.,...&Guo, Qinghua.(2022).Loess Landslide Detection Using Object Detection Algorithms in Northwest China.REMOTE SENSING,14(5). |
| MLA | Ju, Yuanzhen,et al."Loess Landslide Detection Using Object Detection Algorithms in Northwest China".REMOTE SENSING 14.5(2022). |
入库方式： OAI收割
来源：植物研究所
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