References
- Almohamad, H.A. and Duffuaa, S.O., 1993. A linear programming approach for the weighted graph matching problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15 (5), 522–525. doi:https://doi.org/10.1109/34.211474.
- Bergman, C. and Oksanen, J., 2016. Conflation of OpenStreetMap and mobile sports tracking data for automatic bicycle routing. Transactions in GIS, 20 (6), 848–868. doi:https://doi.org/10.1111/tgis.12192.
- Caelli, T. and Kosinov, S., 2004. An eigenspace projection clustering method for inexact graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26 (4), 515–519. doi:https://doi.org/10.1109/TPAMI.2004.1265866.
- Da Costa, J.N., 2016, June. Towards building data semantic similarity analysis: OpenStreetMap and the polish database of topographic objects. In 2016 Baltic Geodetic Congress (BGC Geomatics), 269–275. Gdansk, Poland: IEEE.
- Duchenne, O., Joulin, A., and Ponce, J., 2011, November. A graph-matching kernel for object categorization. In 2011 International Conference on Computer Vision, 1792–1799. Barcelona, Spain: IEEE.
- Fan, H., et al., 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. doi:https://doi.org/10.1080/13658816.2015.1100732.
- Fan, H., et al., 2014. Quality assessment for building footprints data on OpenStreetMap. International Journal of Geographical Information Science, 28 (4), 700–719. doi:https://doi.org/10.1080/13658816.2013.867495.
- Feng, W., et al., 2013. A spectral-multiplicity-tolerant approach to robust graph matching. Pattern Recognition, 46 (10), 2819–2829. doi:https://doi.org/10.1016/j.patcog.2013.03.003.
- Fu, Z., et al., 2018. A moment-based shape similarity measurement for areal entities in geographical vector data. ISPRS International Journal of Geo-Information, 7 (6), 208. doi:https://doi.org/10.3390/ijgi7060208.
- Goesseln, G. and Sester, M., 2005. Change detection and integration of topographic updates from ATKIS to geoscientific data sets. In: A. Peggy and C. Arie, eds. Next generation geospatial information. London: CRC Press, 85–100.
- Goodchild, M.F. and Li, L., 2012. Assuring the quality of volunteered geographic information. Spatial Statistics, 1, 110–120. doi:https://doi.org/10.1016/j.spasta.2012.03.002
- Haklay, M., 2010. How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environment and Planning B: Planning and Design, 37 (4), 682–703. doi:https://doi.org/10.1068/b35097.
- Hamming, R.W., 1950. Error detecting and error correcting codes. The Bell System Technical Journal, 29 (2), 147–160. doi:https://doi.org/10.1002/j.1538-7305.1950.tb00463.x.
- Kim, J. and Yu, K., 2015. Areal feature matching based on similarity using CRITIC method. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 75. doi:https://doi.org/10.5194/isprsarchives-XL-2-W4-75-2015
- Leung, Y., Meng, D., and Xu., Z., 2013. Evaluation of a spatial relationship by the concept of intrinsic spatial distance. Geographical Analysis, 45 (4), 380–400. doi:https://doi.org/10.1111/gean.12021.
- Li, L. and Goodchild, M.F., 2011. An optimisation model for linear object matching in geographical data conflation. International Journal of Image and Data Fusion, 2 (4), 309–328. doi:https://doi.org/10.1080/19479832.2011.577458.
- Li, Y., et al., 2018. A holistic approach to aligning geospatial data with multidimensional similarity measuring. International Journal of Digital Earth, 11 (8), 845–862. doi:https://doi.org/10.1080/17538947.2017.1359688.
- Liu, L., et al., 2018. M: N Object matching on multiscale datasets based on MBR combinatorial optimization algorithm and spatial district. Transactions in GIS, 22 (6), 1573–1595. doi:https://doi.org/10.1111/tgis.12488.
- Miller, H.J. and Wentz, E.A., 2003. Representation and spatial analysis in geographic information systems. Annals of the Association of American Geographers, 93 (3), 574–594. doi:https://doi.org/10.1111/1467-8306.9303004.
- Neira, J. and Tardós, J.D., 2001. Data association in stochastic mapping using the joint compatibility test. IEEE Transactions on Robotics and Automation, 17 (6), 890–897. doi:https://doi.org/10.1109/70.976019.
- Park, H.M. and Yoon, K.J., 2017. Multi-attributed graph matching with multi-layer graph structure and multi-layer random walks. IEEE Transactions on Image Processing, 27 (5), 2314–2325. doi:https://doi.org/10.1109/TIP.2017.2779264.
- Qi, H.B., Li, Z.L., and Chen, J., 2010. Automated change detection for updating settlements at smaller-scale maps from updated larger-scale maps. Journal of Spatial Science, 55 (1), 133–146. doi:https://doi.org/10.1080/14498596.2010.487855.
- Samal, A., Seth, S., and Cueto, K., 2004. A object-based approach to conflation of geospatial sources. International Journal of Geographical Information Science, 18 (5), 459–489. doi:https://doi.org/10.1080/13658810410001658076.
- Theobald, D.M., 2001. Topology revisited: representing spatial relations. International Journal of Geographical Information Science, 15 (8), 689–705. doi:https://doi.org/10.1080/13658810110074519.
- Tversky, A., 1977. Features of similarity. Psychological Review, 84 (4), 327. doi:https://doi.org/10.1037/0033-295X.84.4.327.
- Umeyama, S., 1988. An eigendecomposition approach to weighted graph matching problems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10 (5), 695–703. doi:https://doi.org/10.1109/34.6778.
- Van Wyk, B.J. and Van Wyk, M.A., 2003. Kronecker product graph matching. Pattern Recognition, 36 (9), 2019–2030. doi:https://doi.org/10.1016/S0031-3203(03)00009-8.
- Van Wyk, M.A., Durrani, T.S., and Van Wyk, B.J., 2002. A RKHS interpolator-based graph matching algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (7), 988–995. doi:https://doi.org/10.1109/TPAMI.2002.1017624.
- Wang, H., et al., 2013. Geometric matching method of area feature based on multi-weighted operators. Geomatics & Information Science of Wuhan University, 38, 1671–8860.
- Xavier, E., Ariza-López, F.J., and Ureña-Cámara, M.A., 2016. A survey of measures and methods for matching geospatial vector datasets. ACM Computing Surveys (CSUR), 49 (2), 39. doi:https://doi.org/10.1145/2963147.
- Xu, Y., et al., 2017. Shape similarity measurement model for holed polygons based on position graphs and Fourier descriptors. International Journal of Geographical Information Science, 31 (2), 253–279. doi:https://doi.org/10.1080/13658816.2016.1192637.
- Yuan, S. and Tao, C., 1999. Development of conflation components. Proceedings of Geoinformatics, 99, 1–13.
- Zhang, W.B., Leung, Y., and Ma, J.H., 2019. Analysis of positional uncertainty of road networks in volunteered geographic information with a statistically defined buffer-zone method. International Journal of Geographical Information Science, 33 (9), 1807–1828. doi:https://doi.org/10.1080/13658816.2019.1606430.
- Zhang, X., et al., 2014. Data matching of building polygons at multiple map scales improved by contextual information and relaxation. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 147–163. doi:https://doi.org/10.1016/j.isprsjprs.2014.03.010
- Zhou, Y., Leung, Y., and Zhang, W.B., 2020. A location-and-form-based distance for geographical analysis. Annals of the Association of American Geographers, 1–18. doi:https://doi.org/10.1080/24694452.2020.1785269.