Automatic matching of multi-scale road network under the constraint of small-scale road mesh

Hongxing Pei; Renjian Zhai; Fang Wu; Jinghan Li; Xianyong Gong; Zheng Wu

doi:10.24294/jgc.v5i2.1677

Automatic matching of multi-scale road network under the constraint of small-scale road mesh

Hongxing Pei, Renjian Zhai, Fang Wu, Jinghan Li, Xianyong Gong, Zheng Wu

Article ID: 1677
Vol 5, Issue 2, 2022

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Abstract

Aiming at the problem of road network multi-scale matching, a multi-scale road matching method under the constraint of road mesh of small-scale data has been proposed. First, two road meshes with different scale data are constructed; Secondly, under the constraint of the small-scale road mesh, the composite mesh composed of several road meshes in the large-scale road is extracted, and the mesh matching with the small-scale road mesh is completed; Then, many-to-many matching of road meshes with different scales is realized; finally, the matching relationship between composite mesh and small-scale road mesh is transformed into the matching between multi-scale road mesh boundary roads and internal roads, and the matching of the whole road network is completed. The experimental results show that this method can better realize the matching of multi-scale road network.

Keywords

Multiscale Matching; Road Network Matching; Road Mesh

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References

1. Cheng J. Study on the integrative organization and representation method for geographical entity in different scales (in Chinese) [MSc thesis]. Hangzhou: Zhejiang University; 2011.

2. Wang Y, Li X, Gong H. On multi-scale representations of geographic features (in Chinese). Science in China Series E: Technological Sciences 2006; 49(S2): 39–47.

3. Balley S, Parent C, Spaccapietra S. Modelling geographic data with multiple representations. International Journal of Geographical Information Science 2004; 18(4): 327–352.

4. Ai T, Cheng J. Key issues of multi-scale representation of spatial data (in Chinese). Geomatics and Information Science of Wuhan University 2005; 30(5): 377–382.

5. Zhang Q, Wu F, Qian H, et al. Milestone scales oriented spatial data multi-representation techniques (in Chinese). Journal of Geomatics Science and Technology 2011; 28(5): 383–386.

6. Wei H. The research and application of multiscale geographic database in GIS (in Chinese). Journal of Institute of Surveying and Mapping 2000; 17(2): 134–137.

7. Wu F, Zhang Q, Gong X, et al. Matching and classification model for multi-scale transformation and representation of spatial data (in Chinese). Journal of Geomatics Science and Technology 2014; 31(4): 331–335.

8. Xu F, Deng M, Zhao B, et al. A detailed investigation on the methods of object matching (in Chinese). Journal of Geo-Information Science 2009; 11(5): 657–663.

9. Chen J, Qian H, Wang X, et al. Improving the matching rate of line feature by using dynamic simplification (in Chinese). Acta Geodaetica et Cartographica Sinica 2016; 45(4): 486–493.

10. Walter V, Fritsh D. Matching spatial data sets: a statistical approach. International Journal of Geographical Information Science 1999; 13(5): 445–473.

11. Zhai R. Research on automated matching methods for multi-scale vector spatial data based on global consistency evaluation (in Chinese) [PhD thesis]. Zhengzhou: Information Engineering University; 2011.

12. Saalfeld A. Conflation automated map compilation. International Journal of Geographical Information Systems 1988; 2(3): 217–218.

13. Zhang M, Shi W, Meng L (editors). A generic matching algorithm for line networks of different resolutions. Proceedings of the 8th ICA Workshop on Generalisation and Multiple Representation; 2005 July 7–8; A Coruna. A Coruna: ICC; 2005. p. 10.

14. Volz S (eiditor). An iterative approach for matching multiple representations of street data. In: Hampe M, Sester M, Harrie L (editors). Multiple representation and interoperability of spatial data. Session 6: Matching. Hanover, Germany: ISPRS; 2006. 101-110.

15. Goesseln GV (editor). A matching approach for the integration, change detection and adaptation of heterogeneous vector data sets; 2005 Jul 9-16; A Coruña. XXII International Cartography Conference. A Coruña, Spain: International Cartographic Association; 2005.

16. Liu H, Qian H, Wang X, et al. Road networks global matching method using analytical hierarchy process (in Chinese). Geomatics and Information Science of Wuhan University 2015; 40(5): 644–651.

17. Chen Y, Gong J, Shi W. A distance-based matching algorithm for multi-scale road networks (in Chinese). Acta Geodaetica et Cartographica Sinica 2007; 36(1): 84–90.

18. Zhao D, Sheng Y. Research on automatic matching of vector road networks based on global optimization (in Chinese). Acta Geodaetica et Cartographica Sinica 2010; 39(4): 416–421.

19. Luan X, Yang B, Li Q. Pattern-based node matching approach for road networks (in Chinese). Acta Geodaetica et Cartographica Sinica 2013; 42(4): 608–614.

20. Zhao H. Extracting and application on the classification of urban roads mesh tree (in Chinese) [MSc thesis]. Beijing: Beijing University of Civil Engineering and Architecture; 2014.

21. Zhai R, Wu F, Huang B, et al. A method for recognition and representation of areal hierarchy of urban road networks (in Chinese). Journal of Geomatics Science and Technology 2014; 31(4): 413–418.

22. Xu Z, Liu C, Zhang H, et al. Road selection based on evaluation of stroke network functionality (in Chinese). Acta Geodaetica et Cartographica Sinica 2012; 41(5): 769–776.

23. Thomson RC, Richardson DE (editors). The “good continuation” principle of perceptual organization applied to the generalization of road networks. Proceedings of the 19th International Cartographic Conference; 1999 Aug 14-21; Ottawa. Singapore: ICA; 1999.

24. Thomson RC, Brooks R (editors). Efficient generalization and abstraction of network data using perceptual grouping. Proceedings of the 5th International Conference on GeoComputation; 2000 Aug 23-25; Manchester. London: University of Greenwich; 2000.

25. Huttenlocher DP, Klandermanga GA, Rucklidge WJ. Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 1993; 15(9): 850–863.

DOI: https://doi.org/10.24294/jgc.v5i2.1677