# Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method
###### Abstract  
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. Keywords:ropeway of cable car; adaptation; segmentation and extraction; spatial distribution characteristics; least squares-principal component analysis; extraction accuracy +
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highly intelligent, convenient to operate, low cost, and easy to implement [11; 12]. However, current mainstream image-based detection methods have many limitations, including the difficulty of segmenting cableways due to complex backgrounds and the inability to identify them due to blurry images or low pixel counts [13; 14]. Therefore, based on the high-precision point cloud data collected by the UAV-borne 3D lidar system, many of the limitations of other detection methods can be overcome through its high positioning accuracy and strong anti-interference capability [15; 16; 17; 18]. Many scholars have recently conducted research on the segmentation, detection, and information extraction of airborne lidar point clouds [19; 20; 21; 22]. A support vector machine (SVM) classifier was used by [PERSON] et al. to identify cables, but there was a problem with misclassification when vegetation was planted below the cables [23]. The algorithm developed by [PERSON] et al. began with meshing the point cloud, was then transformed using the Hough transform, and then point cloud features were used to extract cables, but the robustness of the algorithm was heavily reliant on the density of the point cloud [24]. A spatial grid and clustering method were used by [PERSON] et al. to extract cables; however, this method requires a large number of empirical values and cannot be fully automated [25]. The filtering method was used by [PERSON] et al. in order to eliminate ground points, vegetation points, and building points; a Hough transform was then applied in order to separate a single cable [26]. This method will have classification errors regarding vegetation or other small objects under the cable. Based on the spatial dimension characteristics of the cable combined with the Hough transform method, [PERSON] et al. were able to extract the cable after filtering out the ground points; however, this method requires calculating eigenvalues for each point, and there is incomplete segmentation at the rod body connection [27]. Furthermore, research on orthogonal polynomials will assist in the extraction of cable characteristics [28; 29]. A method for high-precision segmentation and extraction of ropoways based on an adaptive algorithm is proposed in this paper in response to the difficult problem of ropeway observation and maintenance.