A Study on a Matching Algorithm for Urban Underground Pipelines
Abstract
:1. Introduction
2. Related Work
3. Holistic Stroke Matching
3.1. Geometric Similarity
3.2. Structural Similarity
4. Partial Stroke Matching
4.1. Stroke Partial Matching Algorithm Based on Segment Decomposition (SPMA-S)
4.2. Stroke Partial Matching Algorithm Based on Vertex Decomposition (SPMA-V)
5. Experiment
5.1. Experimental Data and Matching Process
5.2. Comparative Experiments and Evaluation
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Shi, L.; Liu, Z.; Fu, J.; Yan, S. Research on the utility tunnel location technology based on ZigBee communication network. Ce Ca 2017, 42, 167–171. [Google Scholar]
- Eskandari, M.; Omidvar, B.; Modiri, M.; Nekooie, M.A.; Alesheikh, A.A. Geospatial Analysis of Earthquake Damage Probability of Water Pipelines Due to Multi-Hazard Failure. ISPRS Int. J. Geo-Inf. 2017, 6, 169. [Google Scholar] [CrossRef]
- Li, W.; Han, Y.; Liu, Y.; Zhu, C.; Ren, Y.; Wang, Y.; Chen, G. Real-Time Location-Based Rendering of Urban Underground Pipelines. ISPRS Int. J. Geo-Inf. 2018, 7, 32. [Google Scholar] [CrossRef]
- Li, S.; Cheng, C.; Pu, G.; Chen, B. QRA-Grid: Quantitative Risk Analysis and Grid-based Pre-warning Model for Urban Natural Gas Pipeline. ISPRS Int. J. Geo-Inf. 2019, 8, 122. [Google Scholar] [CrossRef]
- Gong, M.X.; Yuan, S.; Chu, Z.W.; Zhang, S.L.; Fang, C.L. Underground Pipeline Data Matching Considering Multiple Spatial Similarities. Acta Geod. Cartogr. Sin. 2015, 44, 1392–1400. [Google Scholar]
- Chen, Y.M.; Gong, J.Y.; Shi, W.Z. A Distance-based Matching Algorithm for Multi-scale Road Networks. Acta Geo. Carto. Sin. 2007, 36, 84–90. [Google Scholar]
- Deng, M.; Li, Z.L.; Chen, X.Y. Extended Hausdorff distance for spatial objects in GIS. Int. J. Geograph. Inform. Sci. 2007, 21, 459–475. [Google Scholar]
- Davis, M. JCS Conflation Suite Technical Report. 2003. Available online: http://www.vividsolutions.com/JCS/main.htm (accessed on 1 January 2003).
- Tong, X.H.; Deng, S.S.; Shi, W.Z. A Probabilistic Theory-based Matching Method. Acta Geod. Cartogr. Sin. 2007, 36, 210–217. [Google Scholar]
- Liu, H.; Qian, H.; Wang, X. Road Network Global Matching Method using Analytical Hierarchy Process. Geomat. Inform. Sci. Wuhan Univ. 2015, 40, 644–651. [Google Scholar]
- Saalfeld, A. Conflation: Automated Map Compilation. Int. J. Geogr. Inform. Sys. 1988, 2, 217–218. [Google Scholar] [CrossRef]
- Thomson, R.C.; Brooks, R. Exploiting Perceptual Grouping for Map Analysis, Understanding and Generalization: The Case of Road and River Networks. In Graphics Recognition Algorithms and Applications: 4th International Workshop, GREC 2001 Kingston, Ontario, Canada, September 7–8, 2001 Selected Papers; Springer: Berlin/Heidelberg, Germany, 2001; Volume 2390, pp. 148–157. [Google Scholar]
- Thomson, R.C. The ’stroke’ Concept in Geographic Network Generalization and Analysis. In Progress in Spatial Data Handling; Springer: Berlin/Heidelberg, Germany, 2006; pp. 681–697. [Google Scholar]
- Zhang, J.; Wang, Y.; Zhao, W. An Improved Hybrid Method for Enhanced Road Feature Selection in Map Generalization. ISPRS Int. J. Geo-Inf. 2017, 6, 196. [Google Scholar] [CrossRef]
- Xavier, E.M.A.; Ariza-López, F.J.; Ureña-Cámara, M.A. A survey of measures and methods for matching geospatial vector datasets. ACM Comput. Surv. 2016, 49, 1–34. [Google Scholar] [CrossRef]
- Walter, V.; Fritsch, D. Matching Spatial Data Sets: A Statistical Approach. Int. J. Geogr. Inform. Sys. 1999, 13, 445–473. [Google Scholar] [CrossRef]
- Olteanu-Raimond, A.M.; Mustière, S.; Ruas, A. Knowledge formalization for vector data matching using belief theory. J. Spat. Inf. Sci. 2015, 10, 21–46. [Google Scholar] [CrossRef]
- Chehreghan, A.; Abbaspour, R.A. An assessment of spatial similarity degree between polylines on multi-scale, multi-source maps. Geocarto Int. 2017, 32, 471–487. [Google Scholar] [CrossRef]
- Beeri, C.; Kan-za, Y.; Safra, E.; Sagiv, Y. Object Fusion in Geographic Information Systems. In Proceedings of the 30th VLDB Conference, Toronto, ON, Canada, 31 August–3 September 2004; pp. 816–827. [Google Scholar]
- Badard, T. On the Automatic Retrieval of Updates in Geographic Databases Based on Geographic Data Matching Tools. Bulletin du Comit Franais de Cartographie. 1999, 162, 34–40. [Google Scholar]
- An, X.; Sun, Q.; Yu, B. Feature Matching from Network Data at Different Scales Based on Similarity Measure. Wuhan Daxue Xuebao 2012, 37, 219–224. [Google Scholar]
- Filin, S.; Doytsher, Y. Detection of Corresponding Objects in a Linear-Based Map Conflation. Survey. Land Inform. Sys. 2000, 60, 117–128. [Google Scholar]
- Abdolmajidi, E.; Mansourian, A.; Will, J.; Harrie, L. Matching Authority and VGI Road Networks using an Extended Node-based Matching Algorithm. Geospat. Inform. Sci. 2015, 18, 65–80. [Google Scholar] [CrossRef]
- Veltkamp, R.C. Shape Matching: Similarity Measures and Algorithms. In Proceedings of the International Conference on Shape Modeling and Applications, Genova, Italy, 7–11 May 2001. [Google Scholar]
- Volz, S. An Iterative Approach for Matching Multiple Representations of Street Data. In Proceedings of the ISPRS Workshop-Multiple Rep. Interoperability of Spatial Data, Hannover, Germany, 22–24 February 2006; Volume 36, pp. 22–24. [Google Scholar]
- Koukoletsos, T.; Haklay, M.; Ellul, C. Assessing Data Completeness of VGI through an Automated Matching Procedure for Linear Data. Trans. GIS 2012, 16, 477–498. [Google Scholar] [CrossRef]
- Samal, A.; Seth, S.; Cueto, K. A feature-based approach to conflation of geospatial sources. Int. J. Geogr. Inform. Sci. 2004, 18, 459–489. [Google Scholar] [CrossRef]
- Zhang, M.; Meng, L. Delimited Stroke Oriented Algorithm-Working Principle and Implementation for the Matching of Road Networks. Ann. GIS 2008, 14, 44–53. [Google Scholar] [CrossRef]
- Zhai, R. Research on Automated Matching Methods for Multi-Scale Vector Spatial Data Based on Global Consistency Evaluation. Ph.D. Thesis, Information Engineering University, Zhengzhou, China, 2011. [Google Scholar]
- Wang, X.; Qian, H.; He, H.; Chen, J.; Hu, H. Matching Method for Road Networks Considering the Similarity of the Neighborhood Habitation Cluster. Acta Geod. Cartogr. Sin. 2016, 45, 103–111. [Google Scholar]
- Fan, H.; Yang, B.; Zipf, A.; Rousell, A. A Polygon-based Approach for Matching OpenStreetMap Road Networks with Regional Transit Authority data. Int. J. Geogr. Inform. Sci. 2016, 30, 748–764. [Google Scholar]
- Hu, Y. Matching of Roads under Different Scales for Updating Map Data. Geomat. Inform. Sci. Wuhan Univ. 2010, 35, 451–456. [Google Scholar]
- Liu, C.; Qian, H.; Wang, X.; He, H.; Chen, J. A Linkage Matching Method for Road Networks Considering the Similarity of Upper and Lower Spatial Relation. Acta Geod. Cartogr. Sin. 2016, 45, 1371–1383. [Google Scholar]
- Mantel, D.; Lipeck, U. Matching Cartographic Objects in Spatial Databases. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences; ISPRS: Istanbul, Turkey, 2004; Volume XXXV, pp. 12–23. [Google Scholar]
- Zhang, M.; Meng, L. An Iterative Road-matching Approach for the Integration of Postal Data. Comp. Environ. Urban Sys. 2007, 31, 597–615. [Google Scholar] [CrossRef]
- Zhao, D.; Sheng, Y. Research on Automatic Matching of Vector Road Networks Based on Global Optimization. Acta Geod. Cartogr. Sin. 2010, 39, 416–421. [Google Scholar]
- Song, W.; Keller, J.M.; Haithcoat, T.L.; Davis, C.H. Relaxation-Based Point Feature Matching for Vector Map Conflation. Trans. GIS 2011, 15, 43–60. [Google Scholar] [CrossRef]
- Zhang, Y.; Yang, B.; Luan, X. Automated Matching Crowdsourcing Road Networks Using Probabilistic Relaxation. Acta Geod. Cartogr. Sin. 2012, 4, 281–286. [Google Scholar] [CrossRef]
- Yang, B.; Zhang, Y.; Luan, X. A Probabilistic Relaxation Approach for Matching Road Networks. Int. J. Geogr. Inform. Sci. 2013, 27, 319–338. [Google Scholar] [CrossRef]
- Yang, L.; Zuo, Z.; Wang, R. Matching Road Network Combining Hierarchical Strokes and Probabilistic Relaxation Method. Open Auto. Contr. Sys. J. 2014, 6, 268–276. [Google Scholar] [CrossRef] [Green Version]
- Tong, X.; Liang, D.; Jin, Y. A Linear Road Object Matching Method for Conflation Based on Optimization and Logistic Regression. Int. J. Geogr. Inform. Sci. 2014, 28, 824–846. [Google Scholar] [CrossRef]
- Fu, Z.; Yang, Y.; Gao, X. Road Networks Matching Using Multiple Logistic Regression. Geomat. Inform. Sci. Wuhan Univ. 2016, 41, 171–177. [Google Scholar]
- Mustière, S. Results of experiments on Automated Matching of Networks at Different Scales. In Proceedings of the ISPRS Workshop-Multiple Resentation and Interoperability of Spatial Data, Hannover, Germany, 22–24 February 2006; pp. 92–100. [Google Scholar]
- Mustière, S.; Devogele, T. Matching Networks with Different Levels of Detail. GeoInformatica 2008, 12, 435–453. [Google Scholar] [CrossRef]
- Zhang, L.G.; Wu, J.Q.; Gao, W. Hand Gesture Recognition Based on Hausdorff Distance. J. Imag. Graph. 2002, 7, 1144–1150. [Google Scholar]
- Bruns, H.T.; Egenhofer, M.J. Similarity of spatial scenes. In Proceedings of the 7th Symposium on Spatial Data Handling, Delft, The Netherlands, 12–16 August 1996; pp. 173–184. [Google Scholar]
Study Data | Data Sources | Data Format | Number of Pipelines | Total Length (m) |
Integrated pipeline (gas) | Institute of surveying and mapping | MDB | 1138 | 11,255.573 |
Professional pipeline (gas) | The gas company | MDB | 1274 | 11,966.123 |
Integrated Pipeline Segment | Number of Integrated Pipeline Segments | Professional Pipeline Segment | Number of Professional Pipeline Segments | Matching Type |
---|---|---|---|---|
661–662 | 1 | TR412–TR413 | 1 | 1:1 |
906–905 | 2 | TR679–TR680 | 1 | n:1 |
421–117 | 2 | 1TR2003–1TR1108 | 3 | n:m |
Algorithm Step | f(C) | f(W) | f(U) | P(%) | R(%) | Time Consumed (s) |
---|---|---|---|---|---|---|
Holistic Stroke Matching | 578 | 0 | 379 | 100.0 | 60.4 | 2.94 |
SPMA-S Matching | 192 | 47 | 3 | 80.3 | 98.5 | 1.43 |
SPMA-V Matching | 187 | 25 | 8 | 88.2 | 95.9 | 1.16 |
Algorithm Type | f(C) | f(W) | f(U) | P(%) | R(%) | Time Consumed (s) |
---|---|---|---|---|---|---|
Stroke algorithm | 957 | 72 | 11 | 93.0 | 98.8 | 6.53 |
Node–segment algorithm | 918 | 97 | 25 | 90.4 | 97.3 | 7.95 |
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Wang, S.; Guo, Q.; Xu, X.; Xie, Y. A Study on a Matching Algorithm for Urban Underground Pipelines. ISPRS Int. J. Geo-Inf. 2019, 8, 352. https://doi.org/10.3390/ijgi8080352
Wang S, Guo Q, Xu X, Xie Y. A Study on a Matching Algorithm for Urban Underground Pipelines. ISPRS International Journal of Geo-Information. 2019; 8(8):352. https://doi.org/10.3390/ijgi8080352
Chicago/Turabian StyleWang, Shuai, Qingsheng Guo, Xinglin Xu, and Yuwu Xie. 2019. "A Study on a Matching Algorithm for Urban Underground Pipelines" ISPRS International Journal of Geo-Information 8, no. 8: 352. https://doi.org/10.3390/ijgi8080352