Extraction of Power Line Pylons and Wires Using Airborne LiDAR Data at Different Height Levels

Abstract

High density airborne point cloud data have become an important means for modelling and maintenance of power line corridors (PLCs). As the amount of data in a dense point cloud is large, even in a small area, automatic detection of pylon locations can offer a significant advantage by reducing the number of points that need to be processed in subsequent steps, i.e., the extraction of individual pylons and wires. However, the existing solutions mostly overlook this advantage by processing all of the available data at one time, which hinders their application to large datasets. Moreover, the presence of high vegetation and hilly terrain may challenge many of the existing methods, since vertically overlapping objects (e.g., trees and wires) may not be effectively segmented using a single height threshold. For extraction of pylons and wires, this paper proposes a novel approach which involves converting the input points at different height levels into binary masks. Long straight lines are extracted from these masks and convex hulls around the lines at individual height levels are used to form series of hulls across the height levels. The series of hulls are then projected onto a horizontal plane to form individual corridors. A number of height gaps, where there are no objects between the vegetation and the bottom-most wire, are then estimated. The height gaps along with the height levels consider the presence of hilly terrain as well as high vegetation within the PLCs. By using only the non-ground points within the extracted corridors and height gaps, the pylons are detected. The estimated height gaps are further exploited to define robust seed regions for the detected pylons. The seed regions thereafter are grown to extract the complete pylons. Finally, only the points between the locations of two successive pylons are used to extract points of individual wires. It first counts the number of wires within a power line span and, then, iteratively obtains individual wire points. When tested on two large Australian datasets, the proposed approach exhibited high object-based performance (correctness for pylons and wires of 100% and 99.6%, respectively) and high point-based performance (completeness for pylons and wires of 98.1% and 95%, respectively). Moreover, the planimetric accuracy for the detected pylons was 0.10 m. Thus, the proposed approach is demonstrated to be useful in effective extraction and modelling of pylons and wires.

Automatic Extraction of High-Voltage Bundle Subconductors Using Airborne LiDAR Data

“…Awrangjeb [36] proposed a hierarchical approach where PLCs, pylons, and power lines were extracted in order. The PL corridors in the form of straight lines were first extracted by converting the input point cloud data at different height levels into binary images.…”

An improved method for pylon extraction and vegetation encroachment analysis in high voltage transmission lines using LiDAR data

“…A combined connected component and histogram analysis was performed to find the individual pylons. In another work of Awrangjeb [13] the PLC was first extracted in the form of rectangular regions and pylon locations were detected by finding the gap in terms of height between vegetation and power lines. The pylon locations were used as seed regions and grown to extract the complete pylons.…”

Advanced Power Line Diagnostics Using Point Cloud Data—Possible Applications and Limits

“…The authors of the paper [23] present a tower reconstruction method from LiDAR data using stochastic geometry based on a model library. Another paper [24] presents a new approach to the extraction of conductors, towers and power line corridors based on the newly developed method of Height levels. The advantage of this method is a better extraction of vertically overlapping objects, e.g., trees and conductors.…”