Abstract
Abstract. Automatic extraction of power lines has become a topic of great importance in airborne LiDAR data processing for transmission line management. In this paper, we present a new, fully automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) neighbourhood selection, (iii) feature extraction based on spatial topology, and (iv) SVM classification. In a detailed evaluation involving seven neighbourhood definitions, 26 geometric features and two datasets, we demonstrated that the use of multi-scale neighbourhoods for individual 3D points significantly improved the power line classification. Additionally, we showed that the spatial topological features may even further improve the results while reducing data processing time.
Highlights
Inspecting transmission lines to detect and eliminate hidden risks is an important task for urban and rural power supply management and scientific planning (Ahmad et al, 2013; Matikainen et al, 2016)
We proposed a power line classification method that works over complex scenes where vegetation, buildings and transmission lines are mingling in power line corridors
We proposed a novel power line classification methodology based on airborne laser scanning data
Summary
Inspecting transmission lines to detect and eliminate hidden risks is an important task for urban and rural power supply management and scientific planning (Ahmad et al, 2013; Matikainen et al, 2016). Airborne lidar (light detection and ranging) can directly collect high-precision 3D point cloud data of the power line corridor (McManamon, 2012; Glennie et al, 2013). Power lines are usually close to vegetation and buildings and airborne lidar data volume is large, making it difficult to extract the power line points accurately and quickly from lidar point cloud. The research and development of a highly efficient, rapid and automated method for extracting power lines from airborne lidar point cloud data is a critical topic
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