In order to maintain a ski resort efficiently, regular inspections of the cableways are essential. However, there are some difficulties in discovering and observing the cable car cableways in the ski resort. This paper proposes a high-precision segmentation and extraction method based on the 3D laser point cloud data collected by airborne lidar to address these problems. In this method, first, an elevation filtering algorithm is used to remove ground points and low-height vegetation, followed by preliminary segmentation of the cableway using the spatial distribution characteristics of the point cloud. The ropeway segmentation and extraction are then completed using the least squares-principal component combination analysis method for parameter fitting. Additionally, we selected three samples of data from the National Alpine Ski Center to be used as test objects. The real value is determined by the number of point clouds manually deducted by CloudCompare. The extraction accuracy is defined as the ratio of the number of point clouds extracted by the algorithm to the number of point clouds manually extracted. While the environmental complexities of the samples differ, the algorithm proposed in this paper is capable of segmenting and extracting cableways with great accuracy, achieving a comprehensive and effective extraction accuracy rate of 90.59%, which is sufficient to meet the project’s requirements.

Increasing numbers of people have taken up skiing in recent years due to the strong promotion of the Beijing Winter Olympics in 2022 by We Media. Nowadays, the establishment of three-dimensional digital twin snow fields has become an important way to effectively manage and maintain snow fields. However, due to the large altitude difference and the presence of many trees and rocks in ski resorts, traditional methods face difficulties such as low segmentation accuracy and low merging efficiency of segmentation blocks when performing semantic segmentation of ski runs. Consequently, this paper proposes a contour extraction method of artificial ski runs using composite supervoxels based on the characteristics of artificial ski resorts. To begin with, the point cloud data set of ski resort is segmented to get supervoxels; secondly, the difference in elevation between the seed supervoxel and the adjacent connecting block is calculated to determine whether the merging plane is the ground or another plane; then, according to the normal vector angle threshold and the orthogonal distance threshold, the similarity between the current clustering region and adjacent blocks is evaluated; and finally, the region growth algorithm is optimized based on the point cloud supervoxels of ski resorts, in order to reap the benefits of ski track semantic segmentation. And experiments have shown that the proposed method is superior to the other two in terms of segmentation accuracy, efficiency, and robustness, and is suitable for the segmentation and extraction of ski tracks in complex scenes, such as artificial snow fields.

To address the long-term statistical problem of ski-track area in the construction and operation of ski resorts, we propose a new ski-track point cloud boundary extraction method that improves the accuracy of boundary extraction and minimizes the offset of the area error. In this method, all point clouds are first projected onto the fitting plane using the random sample consensus (RANSAC) method. An improved point cloud boundary extraction algorithm is used to triangulate and extract the high-precision ski-track boundary. A discrete Green formula is then used to calculate and count the ski track’s exact area. It is demonstrated through five sets of test experiments that the error offset of the method proposed in this paper is smaller than that of other classical methods, which confirms its benefits and feasibility.