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Article

A Hierarchical Matching Method for Vectorial Road Networks Using Delaunay Triangulation

by 1,2,3, 1,3,*, 3,4, 1, 1 and 1
1
School of Geography and Information Engineering, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China
2
Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR, Shenzhen 518040, China
3
State Key Laboratory of Geo-Information Engineering, Xi’an 710054, China
4
Xi’an Research Institute of Surveying and Mapping, Xi’an 710054, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(9), 509; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090509
Received: 26 June 2020 / Revised: 17 August 2020 / Accepted: 19 August 2020 / Published: 24 August 2020
The primary objective of vectorial road network matching is to identify homonymous roads from two different data sources. Previous methods usually focus on matching road networks with the same coordinate system but rarely with different or unknown coordinate systems, which may lead to nontrivial and nonsystematic deviations (e.g., rotation angle) between homonymous objects. To fill this gap, this study proposes a novel hierarchical road network matching method based on Delaunay triangulation (DTRM). First, the entire urban road network is divided into three levels (L1, L2, L3) by using the principle of stroke. Then, the triangular meshes are constructed from L2, and the minimum matching unit (MMU) in the triangular mesh is used instead of the traditional “node-arc” unit to measure the similarity for the matching of L2. Lastly, a hierarchical matching solution integrating the probabilistic relaxation method and MMU similarity is yielded to identify the matching relationships of the three-level road network. Experiments conducted in Wuhan, China, and Auckland, New Zealand, show that the MMU similarity metrics can effectively calculate the similarity value with different rotation angles, and DTRM has higher precision than the benchmark probability-relaxation-matching method (PRM) and can correctly identify the most matching-relationships with an average accuracy of 89.63%. This study provides a matching framework for road networks with different or even unknown coordinate systems and contributes to the integration and updating of urban road networks. View Full-Text
Keywords: road network matching; nonsystematic bias; Delaunay triangulation; hierarchical; road integration and updating road network matching; nonsystematic bias; Delaunay triangulation; hierarchical; road integration and updating
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MDPI and ACS Style

Zuo, Z.; Yang, L.; An, X.; Zhen, W.; Qian, H.; Dai, S. A Hierarchical Matching Method for Vectorial Road Networks Using Delaunay Triangulation. ISPRS Int. J. Geo-Inf. 2020, 9, 509. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090509

AMA Style

Zuo Z, Yang L, An X, Zhen W, Qian H, Dai S. A Hierarchical Matching Method for Vectorial Road Networks Using Delaunay Triangulation. ISPRS International Journal of Geo-Information. 2020; 9(9):509. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090509

Chicago/Turabian Style

Zuo, Zejun, Lin Yang, Xiaoya An, Wenjie Zhen, Haoyue Qian, and Songling Dai. 2020. "A Hierarchical Matching Method for Vectorial Road Networks Using Delaunay Triangulation" ISPRS International Journal of Geo-Information 9, no. 9: 509. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090509

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