Updating highway asset inventory using airborne LiDAR

• Published in: Measurement : journal of the International Measurement Confederation Vol. 104; pp. 132 - 141
• Main Authors: He, Yi, Song, Ziqi, Liu, Zhaocai
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.07.2017; Elsevier Science Ltd
• Subjects: Airborne LiDAR; ArcGIS-based workflow; Asset management; Culverts; Data acquisition; Data collection; Field experiment; Guide rails; Highway inventory; Highway transportation; Inventory; Lidar; Roads & highways; Traffic control; Traffic management; Traffic signs; Transportation networks; Utility poles; Workflow; Airborne LiDAR; Highway inventory; Asset management; ArcGIS-based workflow; Field experiment
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2017.03.026

•A field experiment is conducted to collect airborne LiDAR data of four highway sections in Utah.•A GIS-based algorithm is developed to process raw LiDAR point cloud data for extracting highway inventory data.•The pros and cons of the airborne LiDAR technology are compared with the mobile counterpart for highway feature extraction. Highway assets, including traffic signs and signals, light poles, guardrails, and culverts, are essential components of transportation networks. They guide, warn, and protect drivers and regulate traffic. To manage and maintain the regular operation of the highway system, state departments of transportation (DOTs) need reliable and up-to-date information about the location and condition of highway features. Various techniques have been employed to collect highway inventory data. These techniques range from the simplest manual inventory method to methods that involve advanced technology, such as light detection and ranging (LiDAR). The focus of this paper is to analyze the capability and strengths of airborne LiDAR in highway inventory data collection. A field experiment was conducted to collect airborne LiDAR data, and an ArcGIS-based workflow was proposed to process the data. The results demonstrate the effectiveness of the proposed workflow as well as the feasibility and high efficiency of airborne LiDAR for highway inventory data collection.