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Road and Roadside Feature Extraction Using Imagery and Lidar Data for Transportation Operation : Volume Ii-3/W4, Issue 1 (11/03/2015)

By Ural, S.

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Book Id: WPLBN0004013861
Format Type: PDF Article :
File Size: Pages 8
Reproduction Date: 2015

Title: Road and Roadside Feature Extraction Using Imagery and Lidar Data for Transportation Operation : Volume Ii-3/W4, Issue 1 (11/03/2015)  
Author: Ural, S.
Volume: Vol. II-3/W4, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2015
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

APA MLA Chicago

Tarko, A., Shan, J., Romero, M. A., & Ural, S. (2015). Road and Roadside Feature Extraction Using Imagery and Lidar Data for Transportation Operation : Volume Ii-3/W4, Issue 1 (11/03/2015). Retrieved from http://www.ebooklibrary.org/


Description
Description: Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, USA. Transportation agencies require up-to-date, reliable, and feasibly acquired information on road geometry and features within proximity to the roads as input for evaluating and prioritizing new or improvement road projects. The information needed for a robust evaluation of road projects includes road centerline, width, and extent together with the average grade, cross-sections, and obstructions near the travelled way. Remote sensing is equipped with a large collection of data and well-established tools for acquiring the information and extracting aforementioned various road features at various levels and scopes. Even with many remote sensing data and methods available for road extraction, transportation operation requires more than the centerlines. Acquiring information that is spatially coherent at the operational level for the entire road system is challenging and needs multiple data sources to be integrated. In the presented study, we established a framework that used data from multiple sources, including one-foot resolution color infrared orthophotos, airborne LiDAR point clouds, and existing spatially non-accurate ancillary road networks. We were able to extract 90.25% of a total of 23.6 miles of road networks together with estimated road width, average grade along the road, and cross sections at specified intervals. Also, we have extracted buildings and vegetation within a predetermined proximity to the extracted road extent. 90.6% of 107 existing buildings were correctly identified with 31% false detection rate.

Summary
ROAD AND ROADSIDE FEATURE EXTRACTION USING IMAGERY AND LIDAR DATA FOR TRANSPORTATION OPERATION

 

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