Utility poles extraction from mobile lidar data in urban area based on density information

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.