Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/rs14051182 | REMOTE SENSING |
Keywords | DocType | Volume |
loess landslide, google earth image, deep learning, automatic identification, object detection | Journal | 14 |
Issue | Citations | PageRank |
5 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanzhen Ju | 1 | 0 | 0.34 |
| Qiang Xu | 2 | 1 | 1.39 |
| Shichao Jin | 3 | 0 | 0.34 |
| Weile Li | 4 | 0 | 4.06 |
| Yanjun Su | 5 | 0 | 2.03 |
| Xiujun Dong | 6 | 0 | 0.68 |
| Qinghua Guo | 7 | 0 | 0.68 |