TLS IMPLEMENTATION FOR URBAN ROAD EVALUATION

Abstract

This study explores the use of Terrestrial Laser Scanning (TLS) technology, specifically the XGRIDS Lixel L2, for urban road evaluation. Traditional methods, such as manual surveys, and newer technologies, like drones, often face challenges in areas with dense vegetation or rough terrain. TLS, which combines LiDAR and SLAM technologies, offers a portable and precise solution for collecting 3D data, making it suitable for complex environments. In this study, both manual and TLS-based surveys were used to evaluate road gradient, width, and pavement condition. The findings confirm that TLS provides high accuracy, achieving 100% for Pavement Condition Index (PCI), 94.15% for road gradient, and 95.86% for road width. Notably, this study identifies ravelling damage, which was previously unreported, and demonstrates that TLS can detect small potholes, reinforcing the high quality of the generated 3D models. While TLS effectively overcomes UAV LiDAR’s limitations in vegetated areas, tree shadows in real-color mode may obscure certain road damages, which can be mitigated by using RGB mode. Despite its higher cost compared to manual methods, TLS offers detailed and reliable road condition assessments, particularly in complex terrains. Future research should focus on optimizing TLS data acquisition, reducing costs, and integrating Machine Learning (ML) to enhance automatic damage detection and classification, further improving road evaluation processes.