A novel landslide identification method for multi-scale and complex background region based on multi-model fusion: YOLO + U-Net

Comprehensive identification of geological hazard risks remains one of the most important tasks in disaster prevention and mitigation. Currently, remote sensing combined with deep learning methods can gradually achieve the recognition of geological hazard risks. However, landslide images taken by satellites, with complex backgrounds and varying scales, particularly small-scale landslide images, are prone to false detections and omissions. The fundamental reason is that the existing models cannot effectively extract the detailed features of landslide images in multi-scale complex backgrounds. To address these challenges, we selected Luding County, Sichuan Province, China, as the study area and created an open and accurate landslide dataset. Geological hazard research experts interpreted and annotated the samples, which contained 230 landslide images with corresponding labels and geographical coordinates for each landslide. We propose a novel deep learning method for landslide identification, which combines YOLO + U-Net. The specific recognition process is as follows: Firstly, the landslide images to be measured are input into the improved YOLOv4 model for the target detection task, and the landslide detection frames are output to find the approximate location of the landslide using the frames. Then, we put forward an innovative method to expand the landslide detection box, so that it retains certain contextual semantic information, except for the detection box, other backgrounds are filled with black, which can shield a part of irrelevant complex background interference, conducive to further recognition. Finally, the improved U-Net semantic segmentation model is used to perform the semantic segmentation task inside the detection frame, and the accurate landslide boundary segmentation results are obtained. In the experiments, we thoroughly discussed and compared four methods: U-Net, improved U-Net, PSP-Net, and YOLO + U-Net. YOLO + U-Net showed a 20.6% improvement in mean IoU for small-scale landslides compared to U-Net, and a 2.08% improvement for landslides in complex backgrounds, with an average improvement of 9.91%. These results indicate that YOLO + U-Net can effectively extract detailed features of landslide images at different scales, improve the ability to recognize landslides in complex backgrounds, and effectively reduce the problem of false detection and omission of landslide image identification.