Identifying Areas of High-risk Vegetation Encroachment on Electrical Powerlines using Mobile and Airborne Laser Scanned Point Clouds

Powerline vegetation encroachment detection is pivotal in averting power outages and forest fires, especially in urban areas where a high density of buildings, roads, and other urban structures complicates detection. While prior research has effectively detected encroachments within point clouds, they largely overlooked the challenges posed by urban environments and did not leverage publicly available urban datasets. These urban intricacies can hinder the precise classification of powerline and vegetation points. This paper proposes a two-stage method for accurately and automatically detecting vegetation encroachment on urban powerlines. The first stage classifies the points in the point cloud as either belonging to one of three classes of points: vegetation, powerlines or background. The classifier model in this stage is chosen based on a comparative analysis of two deep learning models, PointCNN and RandLA-Net. The second stage detects encroachment areas from the detected vegetation and powerline points using a novel Point-Based Encroachment Detection (P-BED) algorithm. This algorithm identifies encroachment areas with high precision using the following steps: sectioning the map, selecting informative sections in the map, voxel-based down-sampling of points in point cloud, and conducting proximity analysis between vegetation and powerlines voxels. The proposed methodology was trained and tested with two publicly available datasets namely, mobile laser-scanned (MLS) and airborne laser-scanned (ALS) datasets. The first stage achieves an F1-scores of 0.98 for classifying background and 0.96 and 0.94 for classifying vegetation and powerline points, respectively. The newly proposed P-BED method successfully detected encroachments with 100% precision and 96.0% recall, showcasing its potential for improved vegetation management and proactive maintenance in urban settings.