<p><strong>Abstract.</strong> Extraction of individual pylons and wires is important for modelling of 3D objects in a power line corridor (PLC) map. However, the existing methods mostly classify points into distinct classes like pylons and wires, but hardly into individual pylons or wires. The proposed method extracts standalone pylons, vegetation and wires from LiDAR data. The extraction of individual objects is needed for a detailed PLC mapping. The proposed approach starts off with the separation of ground and non ground points. The non-ground points are then classified into vertical (e.g., pylons and vegetation) and non-vertical (e.g., wires) object points using the vertical profile feature (VPF) through the binary support vector machine (SVM) classifier. Individual pylons and vegetation are then separated using their shape and area properties. The locations of pylons are further used to extract the span points between two successive pylons. Finally, span points are voxelised and alignment properties of wires in the voxel grid is used to extract individual wires points. The results are evaluated on dataset which has multiple spans with bundled wires in each span. The evaluation results show that the proposed method and features are very effective for extraction of individual wires, pylons and vegetation with 99% correctness and 98% completeness.</p>
Overhead high-voltage conductors are the chief components of power lines and their safety has a strong influence on social and daily life. In the recent decade, the airborne laser scanning (ALS) technique has been widely used to capture the three-dimensional (3D) information of power lines and surrounding objects. Most of the existing methods focused on extraction of single conductors or extracted all conductors as one object class by applying machine learning techniques. Nevertheless, power line corridors (PLCs) are built with multi-loop, multi-phase structures (bundle conductors) and exist in intricate environments (e.g., mountains and forests), and thus raise challenges to process ALS data for extraction of individual conductors. This paper proposes an automated method to extract individual subconductors in bundles from complex structure of PLCs using a combined image- and point-based approach. First, the input point cloud data are grouped into 3D voxel grid and PL points and separated from pylon and tree points using the fact that pylons and trees are vertical objects while power lines are non-vertical objects. These pylons are further separated from trees by employing a statistical analysis technique and used to extract span points between two consecutive pylons; then, by using the distribution properties of power lines in each individual span, the bundles located at different height levels are extracted using image-based processing; finally, subconductors in each bundle are detected and extracted by introducing a window that slides over the individual bundle. The orthogonal plane transformation and recursive clustering procedures are exploited in each window position and a point-based processing is conducted iteratively for extraction of complete individual subconductors in each bundle. The feasibility and validity of the proposed method are verified on two Australian sites having bundle conductors in high-voltage transmission lines. Our experiments show that the proposed method achieves a reliable result by extracting the real structure of bundle conductors in power lines with correctness of 100% and 90% in the two test sites, respectively.
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