Prototyping Automated Framework for Asset Extraction and Characterization From Mobile Lidar Data

Che, Erzhuo; Kamalou, Sahar; Olsen, Michael; Kenneth, Kenney; Jung, Jaehoon

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Prototyping Automated Framework for Asset Extraction and Characterization From Mobile Lidar Data

Supporting Files

2026-08-01
By Che, Erzhuo ; Kamalou, Sahar ; Olsen, Michael

File Language:

English

[PDF - 33.85 MB]

Details

Creators:

Che, Erzhuo ; Kamalou, Sahar ; Olsen, Michael ; Kenneth, Kenney ; Jung, Jaehoon
Corporate Creators:

Oregon State University ; EZDataMD LLC
Corporate Contributors:

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

FHWA-OR-RD-27-05
Resource Type:

Tech Report
Geographical Coverage:

United States ; Colorado
Edition:

Final Report
Corporate Publisher:

Oregon. Department of Transportation. Research Section
Abstract:

To be able to handle the statewide mobile lidar data ODOT collects efficiently, there is a need for a robust workflow with significant automation that can extract many types of features by effectively leveraging various feature extraction tools and algorithms to support applications. In particular, road characterization has been identified as a key application for information extraction from mobile lidar data. In this literature review, the team first provides a brief overview of the model inventory of roadway elements (MIRE 2.0) to help identify the attributes that are of interest as well as feasible to extract from mobile lidar data. Next, a review of the existing commercial software summarizes the common challenges in using these solutions for feature extraction and characterization tasks. Lastly, the existing work related to road characterization is reviewed covering the topics of ground filtering and road extraction, cross slopes and grades, and horizontal curve, followed by a summary of the benefits of using mobile lidar data as well as the challenges of the existing studies.
Format:

PDF
Download URL:

https://rosap.ntl.bts.gov/view/dot/92961/dot_92961_DS1.pdf
File Type:

[PDF - 33.85 MB]
Collection(s):

US Transportation Collection
Main Document Checksum:

urn:sha-512:29cc93d4e3d2bca992d7446b8aacebe4324c169826075f1b5143a084d064b8d389ba7808941509aa09bc5f024faf427de7a1e950c39c3e4b3f1f080d1b85149a

File Language:

English

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