Abstract
Abstract. Utility poles located along roads play a key role in road safety and planning as well as communications and electricity distribution. In this regard, new sensing technologies such as Mobile Terrestrial Laser Scanner (MTLS) could be an efficient method to detect utility poles and other planimetric objects along roads. However, due to the vast amount of data collected by MTLS in the form of a point cloud, automated techniques are required to extract objects from this data. This study proposes a novel method for automatic extraction of utility poles from the MTLS point clouds. The proposed algorithm is composed of three consecutive steps of pre-processing, cable area detection, and poles extraction. The point cloud is first pre-processed and then candidate areas for utility poles are specified based on Hough Transform (HT). Utility poles are extracted by applying horizontal and vertical density information to these areas. The performance of the method was evaluated on a sample point cloud and 98% accuracy was achieved in extracting utility poles using the proposed method.
Highlights
A utility distribution network is vital for transferring electricity from power plants to power consumables (Guan et al, 2016)
This research was evaluated using Mobile Terrestrial Laser Scanner (MTLS) data collected along a 750m urban corridor in Anderson, South Carolina, USA
This paper proposed a method for extracting utility poles from MTLS point clouds by initially dividing the data into smaller sections and eliminating noisy and unneeded pointes from each section
Summary
A utility distribution network is vital for transferring electricity from power plants to power consumables (Guan et al, 2016). Mapping a utility network including poles and cables is vital due to the importance of electricity in modern life. There are a number of considerations when choosing a method for utility pole mapping including accuracy, safety, cost, and time. High resolution aerial images can be accurate data sources, but manual extraction of pole locations is time consuming and conventional ground control surveying is needed to achieve high accuracy (Wen et al, 2019). Conventional surveying is especially tedious for large areas and poses safety risks to personnel due to their proximity to traffic (Shams et al, 2018; Souleyrette et al, 2003)
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