The extraction of traffic signs from Mobile Light Detection and Ranging (LiDAR) point cloud data has become a focal point in transportation research due to the increasing integration of LiDAR technologies. LiDAR, a remote sensing technology, captures detailed three-dimensional point cloud data, offering a comprehensive view of the surrounding environment. Mobile LiDAR systems mounted on vehicles enable efficient data collection, particularly for large-scale road networks. This study aims to develop and refine techniques for extracting traffic signs from Mobile LiDAR point cloud data, essential for enhancing road safety, navigation systems, and intelligent transportation solutions. By leveraging LiDAR technology, new possibilities for automating traffic sign recognition and mapping emerge. The research focuses on detecting traffic signs using Mobile LiDAR point cloud data, employing an intensity-based sign extraction method to identify traffic signs, traffic signals, and other retro-reflective objects. The workflow involves managing LiDAR Aerial Survey (LAS) datasets, including tasks such as merging/splitting, gridding, and detecting high-intensity features. Identified signs are visualized in Google Earth Pro, facilitating their display in Geographic Information Systems (GIS). Furthermore, the study explores point density analysis, establishing connections with potential grid resolutions for additional extraction or analysis, such as road condition assessments or crack detection.

Publication Date


Publication Type



Transportation Technology

Digital Object Identifier


MTI Project



Transportation, Traffic signs, MTLS, Mobile LiDAR, Point Cloud


Other Physical Sciences and Mathematics | Science and Technology Studies | Transportation