# Research on the Calculation and Analysis of Ski-Track Areas Based on Laser Point Clouds
###### Abstract  
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. laser point clouds; ski-track area; boundary extraction; discrete Green formula +
Footnote †: journal: Medical Imaging  
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in ski resorts should take into account key indicators, such as ski-track thickness, slope, and area. A new research direction concerns the use of digital point cloud models to solve the ski-run area, since it is time-consuming and labor-intensive to calculate and analyze the area. Researchers at home and abroad have primarily used remote sensing to conduct area measurement research on ice and snow, vegetation, buildings, etc. There are two main types of measurement methods: 2D image detection and 3D point cloud measurement. The effective image data of the target area are primarily obtained with high-precision smart sensors, such as spaceborne or airborne imaging spectrometers, infrared cameras, and visible light cameras [18; 19; 20]. These image data are used to build a snow-cover model in order to examine snow volume, snow surface area, snow particle size and particle shape [21]. This method, based on the detection and measurement of two-dimensional images, is relatively simple and efficient, but it cannot be used to measure a specific target or location accurately. Additionally, this method of measurement is relatively ineffective and can only be used to update and monitor data on a daily basis. In terms of 3D point cloud measurement, the rapid acquisition of target features and the precise measurement of target areas are mainly carried out by means of airborne laser scanning (ALS), unmanned aerial laser scanning (ULS), terrestrial laser scanning (TLS) and mobile laser scanning (MLS) [22; 23; 24]. Nevertheless, the threshold selection standard is not clearly defined and the value of the threshold has a significant impact on the calculated surface area of the point clouds. Additionally, there are some third-party commercial software applications, such as Cyclone (developed by the German company Leic), that can estimate the target area. It is important to note, however, that this method has a low level of automation and often relies on regular graphics to estimate the target area, with a result that is often much larger than the actual value. In general, compared to other forms of surveying and mapping, the 3D laser scanner is an incredibly effective, high-precision, and easily accessible instrument for measuring ski slopes. In spite of this, the existing method of calculating area based on point cloud data faces the problems of a low degree of automation and a large deviation from the true value, which need to be addressed. Considering the problems associated with area measurement using the above methods, the UAV-borne LiDAR system was selected as the main method of collecting data. Our paper proposes an improved algorithm for estimating ski-track point cloud boundaries based on the extracted ski-track point cloud data [25] (DOI: 10.3390/app12115678).