A depth information-based method to enhance rainfall-induced landslide deformation area identification

Abstract

Accurately recognizing landslide deformation regions is important for understanding the mechanisms of landslides and predicting landslide disasters. Using slopes in Dayu County, China, as the research background, this paper simulates slopes using a similar test model test and proposes a new method of automatic identification based on depth information. By comparing the depth information of the initial point cloud with that of the landslide point cloud, the landslide deformation area is preliminarily extracted. The preliminarily extracted landslide deformation area is oversegmented by the k-means clustering algorithm. The slope equation fitted with the initial point cloud data is used to distinguish each segmentation block, and the error caused by the initial segmentation is removed to obtain the final recognition result. For validation, 12 images of landslides were quickly identified. The identification time was approximately 281 s. The average relative error of the method in the X and Y directions is 2.34 % and 0.71 %, respectively. The traditional two-dimensional recognition method, on the other hand, has a recognition time of approximately 720 s; the average relative errors in the X and Y directions are 6.61 % and 3.90 %, respectively. The advantages of our method are listed as follows: 1) It improves the segmentation quality of the landslide deformation area, especially in the deformation boundary. 2) Landslide areas with similar colors but large depth variation can be more accurately identified by using depth information. 3) It has the characteristics of real-time monitoring and fast operation. In addition, the new method is used to identify the area and volume of the landslide in the three states, and the results are in accordance with the theory.