As an important power facility for transmission corridors, automatic three-dimensional (3D) reconstruction of the pylon plays an important role in the development of smart grid. In this study, a novel three-dimensional reconstruction method using airborne LiDAR (Light Detection And Ranging) point cloud is developed and tested. First, a principal component analysis (PCA) algorithm is performed for pylon redirection based on the structural feature of the upper part of a pylon. Then, based on the structural similarity of a pylon, a pylon is divided into three parts that are inverted triangular pyramid lower structures, quadrangular frustum pyramid middle structures, and complex upper or lateral structures. The reconstruction of the inverted triangular pyramid structures and quadrangular frustum pyramid structures is based on prior knowledge and a data-driven strategy, where the 2D alpha shape algorithm is used to obtain contour points and 2D linear fitting is carried out based on the random sample consensus (RANSAC) method. Complex structures’ reconstruction is based on the priori abstract template structure and a data-driven strategy, where the abstract template structure is used to determine the topological relationship among corner points and the image processing method is used to extract corner points of the abstract template structure. The main advantages in the proposed method include: (1) Improving the accuracy of the pylon decomposition method through introducing a new feature to identify segmentation positions; (2) performing the internal structure of quadrangular frustum pyramids reconstruction; (3) establishing the abstract template structure and using image processing methods to improve computational efficiency of pylon reconstruction. Eight types of pylons are tested in this study, and the average error of pylon reconstruction is 0.32 m and the average of computational time is 0.8 s. These results provide evidence that the pylon reconstruction method developed in this study has high accuracy, efficiency, and applicability.
Exploring how human activity impacts land use/cover change (LUCC) is a hot research topic in the field of geography and sustainability management. Researchers have primarily used socioeconomic variables to measure human activity. However, the human activity indexes mainly based on socioeconomic variables have a spatial resolution that is coarser than traditional LUCC datasets, which hinders a deep and comprehensive analysis. In view of these problems, we selected China's Lijiang River Basin as our study area and proposed the use of GPS trajectory data for analyzing the impact of human activity on LUCC from two perspectives: (1) Type of population: we used the kernel density estimation method to extract the spatial distribution of activity intensity of local residents and tourists, investigated their correlation with the LUCC result, and found these two populations have different impacts on each land cover; (2) Flow of population: we used the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a network analysis method to build a flow network of population from raw trajectories, conducted regression analysis with LUCC, and found that the flow of population is an important factor driving LUCC and is sometimes a more important factor than the static distribution of the population. Experimental results validated that the proposed method can be used to uncover the impact mechanism of human activity on LUCC at fine-grained scales and provide more accurate planning and instructions for sustainability management.
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