An Automatic Method for Powerline Extraction From ALS Point Cloud of Powerline Corridors

Powerlines are critical components of powerline corridors, essential for ensuring a stable and reliable power supply. With the increasing adoption of airborne laser scanning (ALS) in corridor inspections, the demand for efficient and automated methods to extract powerline points from point clouds has grown significantly. This article presents a method for automated powerline extraction from ALS-acquired point clouds, addressing key challenges such as large-scale data processing, complex terrains, and precise segmentation of powerlines and associated components. The proposed approach begins with grid-based pylon location detection and span-based organization of massive powerline corridor data, enabling efficient data processing. A robust segmentation technique is then introduced, transforming the task into a connected component identification problem within a mesh generated using the 3-D Alpha Wrapping algorithm to effectively separate powerlines from other objects. To address irregular gaps in powerline point clouds, a graph-cut-based energy minimization method is developed to identify and merge powerline fragments. Furthermore, a novel slice-based segmentation technique is introduced to accurately differentiate powerlines from components such as pylons, insulator strings, and jumper wires. The method was evaluated on nine datasets spanning 80.32 km, covering voltage levels of 220 kV, 500 kV, and 800 kV. It achieved an average recall of 0.9981, precision of 0.9977, and $F_{1}$ score of 0.9979, demonstrating its robustness, adaptability, and high performance across diverse scenarios.