Detection of Railway Masts in Airborne LiDAR Data
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 9
Abstract
Generating an object-oriented, geometric digital twin of an existing railway from its point cloud data (PCD) is a laborious task, needing 10 times more labor hours than scanning the physical asset. The resulting modeling cost counteracts the expected benefits of the twin. This cost and effort can be reduced by automating the process of creating such models. The first perceived challenge to achieving such automation is detecting masts from airborne light detection and ranging (LiDAR) data because their position and function (separating substructure from superstructure) is critical to the subsequent detection of other elements. This paper presents a method that tackles the aforementioned challenge by leveraging the highly regulated and standardized nature of railways. In railway infrastructure, the geometric relations of a unit contain overhead line equipment (OLE) between two mast pairs are consistent and repetitive throughout the track. The proposed method starts with tools for cleaning the PCD and roughly detecting its positioning and orientation. The resulting data sets are then processed by restricting the search region of the masts considering its positions compared with the track centerline. Subsequently, the masts are detected using the Random Sample Consensus (RANSAC) algorithm. The final deliverables of the method include the coordinates of the mast positions, detected point clusters and three-dimensional (3D) models of the masts in Industry Foundation Classes (IFC) format. The method was implemented in a prototype and tested on three railway PCDs with a cumulative length of 18 km. The results indicated that the method achieves an overall detection rate of 94%. This is the first method in automatically detecting masts from airborne LiDAR data.
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Data Availability Statement
Data analyzed during the study were provided by a third party. Requests for data should be directed to the provider indicated in the Acknowledgements. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
This research was funded by the Cambridge Commonwealth, European & International Trust and Bentley Systems UK Ltd. Data used in the experiments were kindly made available by Fugro NL Land B.V.
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Published In
Journal of Construction Engineering and Management
Volume 146 • Issue 9 • September 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Oct 6, 2019
Accepted: Apr 9, 2020
Published online: Jun 26, 2020
Published in print: Sep 1, 2020
Discussion open until: Nov 26, 2020
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