Journal of Geodesy and Geoinformation Science ›› 2021, Vol. 4 ›› Issue (3): 60-71.doi: 10.11947/j.JGGS.2021.0306

• Special Issue • Previous Articles     Next Articles

A Robust Model Fitting-based Method for Transmission Line Extraction from Airborne LiDAR Point Cloud Data

Juntao YANG1,2(),Zhizhong KANG1,3,4(),Zhou YANG1,3,4   

  1. 1. School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
    2. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
    3. Subcenter of International Cooperation and Research on Lunar and Planetary Exploration, Center of Space Exploration, Ministry of Education of The People’s Republic of China, Beijing 100083, China
    4. Shanxi Key Laboratory of Resources, Environment and Disaster Monitoring, Jinzhong 030600, China
  • Received:2020-09-15 Accepted:2021-01-15 Online:2021-09-20 Published:2021-10-09
  • Contact: Zhizhong KANG;
  • About author:Juntao YANG (1991—), male, PhD, majors in surveying and mapping, and the research interests include LiDAR data processing and computer vision. E-mail:
  • Supported by:
    National Natural Science Foundation of China(41872207)


Airborne Light Detection And Ranging (LiDAR) can provide high-quality three-dimensional information for the safety inspection of electricity corridors. However, the robust extraction of transmission lines from airborne point cloud data is still greatly challenging. Therefore, this paper proposes a robust transmission line extraction method based on model fitting from airborne point cloud data. First, the candidate power line generation method based on height information is used to reduce the computational complexity at the subsequent steps and the false positives in the extracted results. Then, on the basis of the block-and-slice-constraint Euclidean clustering, a linear structure recognition method based on RANdom SAmple Consensus (RANSAC) is proposed to produce the initial individual transmission line components. Finally, a robust nonlinear least square-based fitting method is developed for the individual transmission line to generate the parameters of its mathematical model for further optimizing the extraction. Experiments were performed on LiDAR point cloud data captured from the helicopter and Unmanned Aerial Vehicle (UAV) platform. Results indicate that the proposed method can efficiently extract the different types of transmission lines along electricity corridors, with the average precision of approximately 98.1%, the average recall of approximately 95.9%, and the average quality of approximately 94.2%, respectively.

Key words: airborne LiDAR; transmission line extraction; unsupervised method; random sample consensus