Savva, Antonis (University of Cyprus), Papageorgiou, Manos (University of Cyprus), Kyrkou, Christos (University of Cyprus), Kolios, Panayiotis (University of Cyprus), Theocharides, Theocharis (University of Cyprus), Panayiotou, Christos (University of Cyprus)

A LiDAR-Based Method to Identify Vegetation Encroachment in Power Networks with UAVs

Scheduled for presentation during the Regular Session "UAS Applications III" (ThA5), Thursday, June 8, 2023, 10:20−10:40, Room 466

2023 International Conference on Unmanned Aircraft Systems (ICUAS), June 6-9, 2023, Lazarski University, Warsaw, Poland

This information is tentative and subject to change. Compiled on April 19, 2024

Abstract

Vegetation encroachment in power transmission and distribution networks constitutes a major hazard for the environment and the networks' integrity, but also for the society at large, with multifaceted consequences. On many occasions the vegetation near the power lines, in conjunction with the aged infrastructure, caused and spread fires leading to large-scale disasters. To this end, 3D representations are proactively created using LiDAR sensors to identify locations of vegetation encroachment. Of particular interest is the use of UAVs, which propose a cost-effective alternative to employing airplanes. In this study, UAVs were employed to acquire LiDAR data from the power distribution network and a subtractive data-driven methodology is proposed, whereby irrelevant points are discarded, aiming to identify power lines without employing 3D modelling methods. In this context, geometric features are calculated and a rigorous analysis is conducted over the feature set, different classifiers and parameters to investigate the robustness of the proposed approach. Extensive evaluation suggests that the Random Forest classifier is able to identify power lines with high performance (F1-Score=97.74% and Accuracy=99.09%), using both geometric and color-based features, being also robust to the presence of moderate noise and down-sampling levels.