Highway Asset and Pavement Condition Management using Mobile Photogrammetry

Abstract

Highway asset condition is of the utmost importance for transportation maintenance and pedestrian safety. Transportation facility managers must have up-to-date information on the status of all transportation assets to keep the transportation facilities operating at their highest level. Because of the sheer volume of transportation assets, an efficient and affordable data-collection procedure is necessary to gather the as-is status of the assets and create an asset inventory. Some pioneer departments of transportation in the United States use mobile Light Detection and Ranging (LiDAR) to monitor highway assets and pavement condition data. Not only is the laser scanning equipment expensive, but the operator in charge of using the equipment must have special technical knowledge that may not be accessible to every individual. More recently, image-based reconstruction, known as photogrammetry, has emerged as a cheaper and simpler technology than LiDAR. Image-based 3D reconstruction can be done using a digital camera, such as a digital single-lens reflex camera or even a smartphone. This paper presents a full review of various research studies conducted on highway asset management and pavement condition assessment using spatial data modeling by the use of LiDAR and photogrammetry. This paper also presents two case studies to fill the current research gap in highway asset inventorying using photogrammetry. The results show the superiority of mobile LiDAR for highway asset inventorying and the possibility of having photogrammetry as a reliable alternative technology only in favorable illumination conditions.

3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds

“…Hasler et al [ 46 ] provided a performance assessment of a smartphone-based mobile mapping for outdoors, achieving sub-decimeter relative 3D accuracy with a standard smartphone. Farhadmanesh et al [ 10 ] investigated the feasibility of using photogrammetry for highway condition assessment. They performed a highway bridge, traffic sign, and pavement distress assessment, and the results were compared to mobile LiDAR technology for highway asset inventory, demonstrating the superiority of the MLS, yet photogrammetry could be an important and affordable alternative in certain circumstances.…”

“…The point density of the LiDAR-acquired point cloud is defined in angular resolution, and thus, the density on object surfaces declines with the object distance at the rate of 1/range 2 . Therefore, from the various LiDAR platforms, mobile laser scanning systems (MLS), both vehicle and UAS platforms, are considered the primary technology for acquiring adequate 3D point cloud data to support road-related information extraction, see relevant studies [9][10][11].…”

Unmanned Aircraft Systems in Road Assessment: A Novel Approach to the Pavement Condition Index and VIZIR Methodologies

“…These methods now or then incorporate similar technological principles: a device to capture pictures (e.g., cameras) mounted on a vehicle to cover long distances (e.g., van) [21]. More recent frameworks have included navigation units with cameras synchronized with external trigger and laser projectors [22] or mobile Light Detection and Ranging (LiDAR) to monitor highway assets and pavement condition data [23]. The integration of Geographic Information Systems (GIS) and advanced image processing algorithms has further enhanced the capability to analyze and visualize pavement condition data in a more comprehensive and efficient manner [24].…”

A close-range photogrammetric model for tracking and performance-based forecasting earthmoving operations

“…On the other hand, digital photogrammetry, both aerial and terrestrial, has proven to be a remarkable and practical technology in various construction management applications that involve 3D data modelling and measurements extraction. Digital photogrammetry has been adopted in construction management for various applications including building information modelling (Faltýnová et al , 2016; Karachaliou et al , 2019; Skrzypczak et al , 2022; Ulvi, 2021), tracking construction activities (Congress and Puppala, 2019; Dai and Lu, 2010; Li and Liu, 2019; Mahami et al , 2019; Omar et al , 2018) and infrastructures monitoring and maintenance (Farhadmanesh et al , 2021; Jofré-Briceño et al , 2021; Nappo et al , 2021). Almost all of the research conducted in these areas has focused on evaluating and using aerial photogrammetry using unmanned aerial vehicles (UAVs) (i.e.…”