Automated Lidar Data to Develop and Manage Active Transportation Asset Inventories

Jung, Jaehoon; Olsen, Michael J.; Che, Erzhuo; Rastiveis, Heidar; Turkan, Yelda; Parrish, Christopher

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Automated Lidar Data to Develop and Manage Active Transportation Asset Inventories

2026-07-01
By Jung, Jaehoon ; Olsen, Michael J. ; Che, Erzhuo ; ...

File Language:

English

[PDF - 56.54 MB ]

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Creators:

Jung, Jaehoon ; Olsen, Michael J. ; Che, Erzhuo ; Rastiveis, Heidar ; Turkan, Yelda ; Parrish, Christopher
Corporate Creators:

Oregon State University
Corporate Contributors:

Oregon. Department of Transportation. Research Section ; United States. Department of Transportation. Federal Highway Administration
Subject/TRT Terms:

Asset Management Automation Bicycle Lanes Classification Crosswalks Data Collection Geographic Information Systems Inventory Laser Radar Medians Mobile Lidar Photogrammetric Data Photogrammetry Procedures
Publication/ Report Number:

FHWA-OR-RD-27-03
Resource Type:

Tech Report
Geographical Coverage:

United States ; Oregon
Edition:

Final Report
Corporate Publisher:

Oregon. Department of Transportation. Research Section
Abstract:

Pedestrian crosswalks, bike lanes, and medians are key safety devices in a multi-modal highway network. However, there is no statewide map of these assets' locations and types. Such a map would benefit planning, maintenance, safety assessments, and network analyses for ODOT and its partners. Currently, ODOT collects asset information through site visits and surveys, which are labor-intensive and costly across the state's vast road network. GIS databases are created ad-hoc and inconsistently, often by interns. Fortunately, ODOT Engineering Automation regularly collects 3D mobile lidar data, providing geometric and radiometric information. This research reviewed state-of-the-art approaches for detecting and classifying pedestrian crosswalks, bike lanes, and medians, and developed a methodology to extract these features from LiDAR datasets for updating ODOT's GIS platform. The approach was tested on several datasets and showed good extraction performance, requiring minimal manual cleanup. Lastly, a plan is provided for ODOT to implement this research.
Format:

PDF
Collection(s):

US Transportation Collection
Main Document Checksum:

urn:sha-512:651db8d34e1c683cd22f7188df428d1c52cd3e6d41abcf2978b88ef4a761a9540aaab9b9e65907a97036d2eeddc5a0a8aaf1f622074108020af4248e1756e8e6
Download URL:

https://rosap.ntl.bts.gov/view/dot/92752/dot_92752_DS1.pdf
File Type:

[PDF - 56.54 MB ]

File Language:

English

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