A new approach for matching road lines using efficiency rates of similarity measures

Abstract

The lack of common semantic information among corresponding geo-objects in different datasets required new matching approaches based on geometric and topological measures. In this study, a semi-automated matching approach based on the matching capabilities of geometric and topological measures was proposed. In the first stage, after the initial matching performed by a scoring system, the efficiency of each measure on the matching accuracy is evaluated manually by an operator. In the second stage, (1) the score of each measure is updated in accordance with the accuracy distributions. This means that the score of a measure is increased if it is relatively more significant than others. Finally, (2) matching process is repeated with new scores. The proposed approach was tested by matching tree-, cellular-, and hybrid-patterned road lines in municipal, private navigation, and OpenStreetMap datasets. The experimental testing shows that it has satisfactory results both in accuracy and completeness. F-measure is over 86% in hybrid-patterned Bosphorus datasets.  

Keywords

• Object matching
• Similarity measure
• Efficiency ratio
• Geometric integration
• Map conflation

References

1. Araújo T, Pires C, Mestre D, de Queiroz A, Santos V & da Nóbrega T (2019). A Parallel-based Map Matching Approach over Urban Place Records. Anais do XXXIV Simpósio Brasileiro de Banco de Dados, 121-132. Porto Alegre: SBC.
2. Başaraner M (2011). A zone-based iterative building displacement method through the collective use of Voronoi tessellation, spatial analysis and multicriteria decision making. Boletim de Ciências Geodésicas, 17(2), 161-187.
3. Bilgi S, Gulnerman A G, Arslanoğlu B, Karaman H & Öztürk Ö (2019). Complexity measures of sports facilities allocation in urban area by metric entropy and public demand compatibility. International Journal of Engineering and Geosciences, 4(3), 141-148.
4. Chehreghan A & Ali Abbaspour R (2018). A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm. Cartography and Geographic Information Science, 45(3), 255-269.
5. Cobb M A, Chung M J, Foley III H, Petry F E, Shaw K B & Miller H V (1998). A rule-based approach for the conflation of attributed vector data. GeoInformatica, 2(1), 7-35.
6. Fan H, Yang B, Zipf A & Rousell A (2016). A polygon-based approach for matching OpenStreetMap road networks with regional transit authority data. International Journal of Geographical Information Science, 30(4), 748-764.
7. Guo Q, Xu X, Wang Y & Liu J (2019). Combined Matching Approach of Road Networks under Different Scales Considering Constraints of Cartographic Generalization. IEEE Access, 8, 944-956.
8. Hacar M (2019). Yol Ağlarının Geometrik Entegrasyonu için Nesne Eşleme Yöntemlerinin Geliştirilmesi. PhD Thesis, Yildiz Technical University, Istanbul.

9. Hacar M (2020). A rule-based approach for generating urban footprint maps: from road network to urban footprint. International Journal of Engineering and Geosciences, 5(2), 100-108.
10. Hacar M & Gökgöz T (2019a). A conceptual model for geo-object matching. International Symposium on Applied Geoinformatics (ISAG-2019), 7-9 November 2019, Istanbul, Turkey, 97-102.
11. Hacar M & Gökgöz T (2019b). A New, Score-Based Multi-Stage Matching Approach for Road Network Conflation in Different Road Patterns. ISPRS International Journal of Geo-Information, 8(2), 81.
12. Kilic B & Gülgen F (2020). Accuracy and similarity aspects in online geocoding services: A comparative evaluation for Google and Bing maps. International Journal of Engineering and Geosciences, 5(2), 109-119.
13. Koukoletsos T, Haklay M & Ellul C (2012). Assessing data completeness of VGI through an automated matching procedure for linear data. Transactions in GIS, 16(4), 477-498.
14. Lei T & Lei Z (2019). Optimal spatial data matching for conflation: A network flow‐based approach. Transactions in GIS, 23(5), 1152-1176.
15. Li L & Goodchild M F (2011). An optimisation model for linear feature matching in geographical data conflation. International Journal of Image and Data Fusion, 2(4), 309-328.
16. Lynch M & Saalfeld A (1985). Conflation: automated map compilation – a video game approach. Proceedings of Autocarto 7, 11–14 March 1985 Washington, DC, USA, 343-352.
17. Memduhoğlu A & Başaraner M (2018). Possible contributions of spatial semantic methods and technologies to multi-representation spatial database paradigm. International Journal of Engineering and Geosciences, 3(3), 108-118.
18. Mustière S & Devogele T (2008). Matching networks with different levels of detail. GeoInformatica, 12(4), 435-453.
19. Olteanu-Raimond A M, Mustiere S & Ruas A (2015). Knowledge formalization for vector data matching using belief theory. Journal of Spatial Information Science, 2015(10), 21-46.
20. Pourabdollah A, Morley J, Feldman S & Jackson M (2013). Towards an authoritative OpenStreetMap: conflating OSM and OS OpenData national maps’ road network. ISPRS International Journal of Geo-Information, 2(3), 704-728.
21. Rosen B & Saalfeld A (1985). Match criteria for automatic alignment. Proceedings of 7th international symposium on computer-assisted cartography (Auto-Carto 7), 11–14 March 1985 Washington, USA, 1-20.
22. Ruiz J J, Ariza F J, Urena M A & Blázquez E B (2011). Digital map conflation: a review of the process and a proposal for classification. International Journal of Geographical Information Science, 25(9), 1439-1466.
23. Saalfeld A (1988). Conflation automated map compilation. International Journal of Geographical Information System, 2(3), 217-228.
24. Samal A, Seth S & Cueto 1 K (2004). A feature-based approach to conflation of geospatial sources. International Journal of Geographical Information Science, 18(5), 459-489.
25. Song W, Keller J M, Haithcoat T L & Davis C H (2011). Relaxation‐based point feature matching for vector map conflation. Transactions in GIS, 15(1), 43-60.
26. Şen A (2013). The applicability of artificial intelligence methods for the selection/elimination process to the stream networks in cartographic generalization. Doctoral Thesis, Yildiz Technical University, Istanbul, Turkey.
27. Volz S (2006). An iterative approach for matching multiple representations of street data. Proceedings of the ISPRS Workshop on Multiple Representation and Interoperability of Spatial Data, Hannover, Almanya, 22–24 Feb 2006, 36(Part 2/W40), 101–110.
28. Xavier E, Ariza-López F J & Ureña-Cámara M A (2016). A survey of measures and methods for matching geospatial vector datasets. ACM Computing Surveys (CSUR), 49(2), 39.
29. Xiong D & Sperling J (2004). Semiautomated matching for network database integration. ISPRS journal of photogrammetry and remote sensing, 59(1-2), 35-46.
30. Yang B, Luan X & Zhang Y (2014). A pattern‐based approach for matching nodes in heterogeneous urban road networks. Transactions in GIS, 18(5), 718-739.
31. Yuan S & Tao C (1999). Development of conflation components. Proceedings of geoinformatics, 99, 1-13.