RT Journal Article
T1 Automatic detection and characterisation of power lines and their surroundings using LIDAR Data
A1 Yermo, Miguel
A1 Martínez Sánchez, Jorge
A1 García Lorenzo, Óscar
A1 López Vilariño, David
A1 Cabaleiro Domínguez, José Carlos
A1 Fernández Pena, Anselmo Tomás
A1 Fernández Rivera, Francisco
K1 Airborne LiDAR
K1 Power lines
K1 Classification
K1 Power line extraction
AB Light Detection and Ranging (LiDAR) is nowadays one of the most used tools to obtain geospatial data. In this paper, a method to detect and characterise power lines of both high and low voltage and their surroundings from 3D LiDAR point clouds exclusively is proposed. First, to identify points of the power lines a global search of candidate points is carried out based on the height of each point compared to its neighbours. Then, the Hough Transform (HT) is applied on the set of candidate points to extract the catenaries that belong to each power line, allowing the identification of each conductor individually. Finally, conductors located on the same power line are grouped, their geometric characteristics analysed, and the quantitative features of the surroundings are computed. A very high accuracy of power line classification is reached with these methods, while the computational time is optimised by efficient memory usage and parallel implementation of the code
PB International Society for Photogrammetry and Remote Sensing
YR 2019
FD 2019-06-05
LK https://hdl.handle.net/10347/44715
UL https://hdl.handle.net/10347/44715
LA eng
NO Yermo, M., Martínez, J., Lorenzo, O. G., Vilariño, D. L., Cabaleiro, J. C., Pena, T. F., and Rivera, F. F.: AUTOMATIC DETECTION AND CHARACTERISATION OF POWER LINES AND THEIR SURROUNDINGS USING LIDAR DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1161–1168, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1161-2019, 2019
NO Babcock International Group, in the frame of the Civil UAVs Initiative of Xunta de Galicia
NO Consellería de Cultura, Educación e Ordenación Universitaria of Xunta de Galicia (accreditation 2016-2019, ED431G/08 and reference competitive group 2019-2021, ED431C 2018/19) and the European Regional Development Fund (ERDF)
NO Ministerio de Economía, Industria y Competitividad within the project TIN2016-76373-P
DS Minerva
RD 24 abr 2026