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author:

Fang, Lina (Fang, Lina.) [1] (Scholars:方莉娜) | Yang, Bisheng (Yang, Bisheng.) [2] | Chen, Chongcheng (Chen, Chongcheng.) [3] (Scholars:陈崇成) | Fu, Huasheng (Fu, Huasheng.) [4]

Indexed by:

EI Scopus

Abstract:

The demand for automated 3D road bounderies extraction is driven by the importance of maintaining and updating the fundamental geographic data of road for for various applications that support urban planning, traffic control, emergency response. Mobile laser scanning (MLS) as a promising technology for the rapid 3D mapping of road environment, provides a good means to capture every detail along the road corridor, including road boundaries, road markings, trees, buildings, traffic poles. This paper presents a new automatic method to detect road boundaries in MLS point clouds. The geometry and intensity information are both utilized to extract road boundary points from the raw point clouds. Experiments were undertaken to evaluate the validity of the proposed method based on two test dataset captured by Optech's Lynx Mobile Mapper System. The well performances prove it to be a promising solution for extracting 3D road boundaries from MLS point clouds. © 2015 IEEE.

Keyword:

Clustering algorithms Data mining Emergency traffic control Extraction Laser applications Road and street markings Roads and streets Scanning Statistical tests

Community:

  • [ 1 ] [Fang, Lina]Spatial Information Research Center of Fujian, FuZhou University, Fuzhou; 350002, China
  • [ 2 ] [Yang, Bisheng]State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan; 430079, China
  • [ 3 ] [Chen, Chongcheng]Spatial Information Research Center of Fujian, FuZhou University, Fuzhou; 350002, China
  • [ 4 ] [Fu, Huasheng]Fujian Provincial Investigation Design and Research Institute of Water Conservancy and Hydropower, Fuzhou; 350002, China

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Year: 2015

Page: 162-165

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 12

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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