# A New Method of Ski Tracks Extraction Based on Laser Intensity Information
## 1 Introduction  
With the successful holding of the 24 th Beijing Winter Olympic Games in 2022, people are progressively interested in participating in winter sports, especially skiing [1, 2]. In the meantime, researchers and scientific research are becoming increasingly interested in skiing. In recent years, a brand-new high-tech method to collect high-precision 3D point cloud data and extract the needed information of ski tracks by LiDAR has been developed [3, 4, 5, 6]. For the managers of ski resorts, the relevant measurement and statistics (such as thickness, slope et al.) is an important part of daily maintenance. Therefore, a key step is to effectively separate the ski tracks from the background by semantic segmentation of 3D point cloud datasets.  
In addition, as the key technology of point cloud processing, the semantic segmentation of laser point cloud is widely used in artificial intelligence, reverse engineering, target reconstruction, robot, medical image analysis, and other related fields [7, 8]. The point cloud semantic segmentation classifies three-dimensional points according to their local features, classifies points with the same or similar attributes into a set of disjoint points, and points in the same set that have similar features [9]. The semantic segmentation, which is helpful in scene analysis, such as location and object recognition, classification and feature extraction, and object recognition, is one of the key links in point cloud data processing.  
The current recognition method of the target classification for 3D datasets research mainly uses the coordinate information of point clouds [10, 11, 12]. The geometric dimension of the target is calculated by the coordinate information of the point cloud; thus the targetclassification and recognition can be realized. [PERSON] et al. divided the point clouds into independent targets and classified the targets by extracting the volume characteristics of each target [13]. [PERSON] et al. divided the three-dimensional datasets into slices according to height and extracted the length and width features of each slice to detect pedestrian targets [14]. The classification method based on the coordinate information of point clouds is easily affected by the occlusion of target, and if the targets have similar geometric characteristics, they cannot be distinguished effectively. This situation makes these methods unable to effectively separate the ski tracks from the complex background. The LiDAR can accurately record coordinate information and reflection intensity of an object, which can efficiently reflect the reflection ability of the object, and has great application prospects in semantic segmentation for multiscale targets. Generally, the reflection intensity of the target is affected by many factors such as atmospheric conditions, incident azimuth, and emission distance, resulting in different reflection intensities of objects [15; 16; 17; 18; 19]. However, due to artificial snow and its high hardness at ski resorts, the laser reflection is similar. The research on how to extract the parameters of the object characteristics from the intensity information is of great significance in strengthening the utilization of the intensity information and broadening target classification methods.  
In this paper, a composite ski tracks extraction algorithm based on laser point cloud intensity information, which can effectively extract the ski tracks under the complex background and provide convenience for the daily operation and maintenance of ski resorts, is proposed.