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Askit C., ATEŞ D., Bakir I., Seyfeli S., OK A. Ö.

2022 Geoinformation Week: Broadening Geospatial Science and Technology, Virtual, Online, Malaysia, 14 - 17 November 2022, vol.48, pp.41-46 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 48
  • Doi Number: 10.5194/isprs-archives-xlviii-4-w6-2022-41-2023
  • City: Virtual, Online
  • Country: Malaysia
  • Page Numbers: pp.41-46
  • Keywords: Feature Extraction, LiDAR, Obstacle Detection, Power Line Classification, Random Forest
  • Hacettepe University Affiliated: Yes


It has been a challenge for electric power management to automatically extract power lines from LiDAR point clouds. However, environmental and technical issues have made management more challenging in complicated areas where power lines are in close proximity to buildings and/or trees. In this study, the structure and types of the data captured by a LiDAR sensor in regions containing line corridors were analysed. The crucial stage is appropriately identifying from the data the essential parts of a power line corridor route. The point cloud dataset used in the study belongs to the Borssele region in Zeeland, the Netherlands. By manually labelling the dataset, three classes were identified: wire, pylon, and others. For the classification of point clouds, the Random Forest method was utilised. To assess the obstacles posed by the class wire, 5 m, 10 m, and 15 m 3D buffer zones are created. The visual presentation of obstacles within the buffer zone is achieved by assigning them a separate class code and indicating that they are inside and partially within. Based on the results, the correctness values of the classes of wire and others are considered to be satisfactory. However, the class pylon contains points with incorrect labels after the classification. As a result, the accuracy of the pylon class is much lower than the accuracy of the other two classes.