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Article

Identifying Complex Junctions in a Road Network

by 1, 1, 2, 1,3,* and 1,3
1
Faculty of Geosciences and Environment Engineering, Southwest Jiaotong University, Chengdu 611756, China
2
Faculty of Geography and Resource Sciences, Sichuan Normal University, Chengdu 610066, China
3
National Collaborative Innovation Center for Rail Transport Safety, Southwest Jiaotong University, Chengdu 611756, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(1), 4; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010004
Received: 13 October 2020 / Revised: 15 December 2020 / Accepted: 21 December 2020 / Published: 24 December 2020
Automated generalization of road network data is of great concern to the map generalization community because of the importance of road data and the difficulty involved. Complex junctions are where roads meet and join in a complicated way and identifying them is a key issue in road network generalization. In addition to their structural complexity, complex junctions don’t have regular geometric boundary and their representation in spatial data is scale-dependent. All these together make them hard to identify. Existing methods use geometric and topological statistics to characterize and identify them, and are thus error-prone, scale-dependent and lack generality. More significantly, they cannot ensure the integrity of complex junctions. This study overcomes the obstacles by clarifying the topological boundary of a complex junction, which provides the basis for straightforward identification of them. Test results show the proposed method can find and isolate complex junctions in a road network with their integrity and is able to handle different road representations. The integral identification achieved can help to guarantee connectivity among roads when simplifying complex junctions, and greatly facilitate the geometric and semantic simplification of them. View Full-Text
Keywords: generalization; scale; topological relationship; road network; pattern recognition generalization; scale; topological relationship; road network; pattern recognition
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MDPI and ACS Style

Yang, J.; Zhao, K.; Li, M.; Xu, Z.; Li, Z. Identifying Complex Junctions in a Road Network. ISPRS Int. J. Geo-Inf. 2021, 10, 4. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010004

AMA Style

Yang J, Zhao K, Li M, Xu Z, Li Z. Identifying Complex Junctions in a Road Network. ISPRS International Journal of Geo-Information. 2021; 10(1):4. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010004

Chicago/Turabian Style

Yang, Jianting, Kongyang Zhao, Muzi Li, Zhu Xu, and Zhilin Li. 2021. "Identifying Complex Junctions in a Road Network" ISPRS International Journal of Geo-Information 10, no. 1: 4. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010004

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