1. [1] Wang, Y., et al., "A back‐propagation neural network‐based approach for multi‐represented feature matching in update propagation", Transactions in GIS, Vol.19, pp. 964-993, 2015. [ DOI:10.1111/tgis.12138] 2. [2] Samal, A., S. Seth, and K. Cueto 1, "A feature-based approach to conflation of geospatial sources", International Journal of Geographical Information Science, Vol.18, pp. 459-489, 2004. [ DOI:10.1080/13658810410001658076] 3. [3] Lei, T. and Z. Lei, "Optimal spatial data matching for conflation: A network flow‐based approach", Transactions in GIS, Vol.23, pp. 1152-1176, 2019. [ DOI:10.1111/tgis.12561] 4. [4] Fan, H., et al., "A polygon-based approach for matching OpenStreetMap road networks with regional transit authority data", International Journal of Geographical Information Science, Vol.30, pp. 748-764, 2016. [ DOI:10.1080/13658816.2015.1100732] 5. [5] Fan, H., et al., "Quality assessment for building footprints data on OpenStreetMap", International Journal of Geographical Information Science, Vol.28, pp. 700-719, 2014. [ DOI:10.1080/13658816.2013.867495] 6. [6] Wang, Y., et al., "A propagating update method of multi-represented vector map data based on spatial objective similarity and unified geographic entity code", in Cartography from Pole to Pole: Springer, 2014, pp. 139-153. [ DOI:10.1007/978-3-642-32618-9_10] 7. [7] Tong, X., W. Shi, and S. Deng, "A probability-based multi-measure feature matching method in map conflation", International Journal of Remote Sensing, Vol.30, pp. 5453-5472, 2009. [ DOI:10.1080/01431160903130986] 8. [8] Kim, I.-H., C.-C. Feng, and Y.-C. Wang, "A simplified linear feature matching method using decision tree analysis, weighted linear directional mean, and topological relationships", International Journal of Geographical Information Science, Vol.31, pp. 1042-1060, 2017. [ DOI:10.1080/13658816.2016.1267736] 9. [9] Tong, X., D. Liang, and Y. Jin, "A linear road object matching method for conflation based on optimization and logistic regression", International Journal of Geographical Information Science, Vol.28, pp. 824-846, 2014. [ DOI:10.1080/13658816.2013.876501] 10. [10] Chehreghan, A. and R. Ali Abbaspour, "A new descriptor for improving geometric-based matching of linear objects on multi-scale datasets", GIScience & Remote Sensing, Vol.54, pp. 836-861, 2017. [ DOI:10.1080/15481603.2017.1338390] 11. [11] Chehreghan, A. and R. Ali Abbaspour, "A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm", Cartography and Geographic Information Science, Vol.45, pp. 255-269, 2018. [ DOI:10.1080/15230406.2017.1324823] 12. [12] Olteanu-Raimond, A.-M., S. Mustiere, and A. Ruas, "Knowledge formalization for vector data matching using belief theory", Journal of Spatial Information Science, pp. 21-46, 2015. [ DOI:10.5311/JOSIS.2015.10.194] 13. [13] Farahanipooya, A., et al., "Roads matching in a multi-scale spatial database using a least square line", Journal of Geomatics Science and Technology, Vol.3, pp. 87-104, 2013. 14. [14] Li, L. and M. Goodchild, "Automatically and accurately matching objects in geospatial datasets", in Adv. Geo-Spat. Inf. Science: 2012, pp. 71-79. 15. [15] Zhang, X., et al., "A multi-scale residential areas matching method using relevance vector machine and active learning", ISPRS International Journal of Geo-Information, Vol.6, p. 70, 2017. [ DOI:10.3390/ijgi6030070] 16. [16] Fu, Z., et al., "A moment-based shape similarity measurement for areal entities in geographical vector data", ISPRS International Journal of Geo-Information, Vol.7, p. 208, 2018. [ DOI:10.3390/ijgi7060208] 17. [17] Wang, Y., et al., "A PSO-neural network-based feature matching approach in data integration", in Cartography-Maps connecting the world: Springer, 2015, pp. 189-219. [ DOI:10.1007/978-3-319-17738-0_14] 18. [18] Du, H., et al., "A method for matching crowd‐sourced and authoritative geospatial data", Transactions in GIS, Vol.21, pp. 406-427, 2017. [ DOI:10.1111/tgis.12210] 19. [19] Abdolmajidi, E., et al., "Matching authority and VGI road networks using an extended node-based matching algorithm", Geo-Spatial Information Science, Vol.18, pp. 65-80, 2015. [ DOI:10.1080/10095020.2015.1071065] 20. [20] Yang, B., Y. Zhang, and X. Luan, "A probabilistic relaxation approach for matching road networks", International Journal of Geographical Information Science, Vol.27, pp. 319-338, 2013. [ DOI:10.1080/13658816.2012.683486] 21. [21] Zhang, M. and L. Meng, "Delimited stroke oriented algorithm-working principle and implementation for the matching of road networks", Geographic Information Sciences, Vol.14, pp. 44-53, 2008. [ DOI:10.1080/10824000809480638] 22. [22] Kieler, B., et al., "Matching river datasets of different scales", in Advances in GIScience: Springer, 2009, pp. 135-154. [ DOI:10.1007/978-3-642-00318-9_7] 23. [23] Hastings, J., "Automated conflation of digital gazetteer data", International Journal of Geographical Information Science, Vol.22, pp. 1109-1127, 2008. [ DOI:10.1080/13658810701851453] 24. [24] Harrie, L. and A. Hellstrom, "A case study of propagating updates between cartographic data sets", presented at 19th International Cartographic Conference, 11th General Assembly of ICA, Ottawa. 1999. 25. [25] Levenshtein, V.I., "Binary codes capable of correcting deletions, insertions, and reversals". Soviet physics doklady: Soviet :union:, Vol.10, pp.707-710, 1966. 26. [26] Huang, L., et al., "Feature matching in cadastral map integration with a case study of Beijing", presented at 18th International Conference on Geoinformatics, 2010. [ DOI:10.1109/GEOINFORMATICS.2010.5567494] 27. [27] Kim, J. and K. Yu, "Areal feature matching based on similarity using CRITIC method", The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.40, p. 75, 2015. [ DOI:10.5194/isprsarchives-XL-2-W4-75-2015] 28. [28] Zhonglianga, F. and W. Jianhuaa, "Entity matching in vector spatial data", presented at XXI ISPRS Congress. 2008. 29. [29] Mandal, S.N., J.P. Choudhury, and S.B. Chaudhuri, "In search of suitable fuzzy membership function in prediction of time series data", International Journal of Computer Science Issues, Vol.9, pp. 293-302, 2012. 30. [30] Wenjing, T., et al., "Research on areal feature matching algorithm based on spatial similarity", presented at Chinese control and decision conference, 2008. 31. [31] Rucklidge, W., Efficient visual recognition using the Hausdorff distance. Springer-Verlag, 1996. [ DOI:10.1007/BFb0015091] 32. [32] Huh, Y., K. Yu, and J. Heo, "Detecting conjugate-point pairs for map alignment between two polygon datasets", Computers, Environment and Urban Systems, Vol.35, pp. 250-262, 2011. [ DOI:10.1016/j.compenvurbsys.2010.08.001] 33. [33] Ying, S., et al., "Probabilistic matching of map objects in multi-scale space", presented at 25th International Cartographic Conference, 2011. 34. [34] Zhang, X., et al., "Pattern classification approaches to matching building polygons at multiple scales", in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-2, XXII ISPRS Congress, International Society for Photogrammetry and Remote Sensing, pp. 19-24, 2012. [ DOI:10.5194/isprsannals-I-2-19-2012] 35. [35] Yue, H., et al., "A Multi-Scale Settlement Matching Algorithm Based on ARG", International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, Vol.41, 2016. [ DOI:10.5194/isprs-archives-XLI-B2-139-2016] 36. [36] Arkin, E.M., et al., "An efficiently computable metric for comparing polygonal shapes", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.13, 1991. [ DOI:10.21236/ADA235508] 37. [37] Chamani, M., R. Ali Abbaspour, and A.R. Chehreghan, "Matching of Polygon Objects Based on Geometric Measures in a Multi-Scale Dataset", Journal of Geomatics Science and Technology, Vol.7, pp. 73-87, 2018.
|