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Research Article

Graph neural network-based identification of ditch matching patterns across multi-scale geospatial data

ORCID Icon, , ORCID Icon, , &
Article: 2294900 | Received 26 Oct 2023, Accepted 08 Dec 2023, Published online: 26 Dec 2023

References

  • Ai T. 2021. Some thoughts on empowering map making with deep learning. Acta Geod Cartogr Sin. 50(9):1170–1182.
  • Chehreghan A, Ali Abbaspour R. 2017. A new descriptor for improving geometric-based matching of linear objects on multi-scale datasets. GIScience Remote Sens. 54(6):836–861. doi:10.1080/15481603.2017.1338390.
  • Chehreghan A, Ali Abbaspour R. 2018. A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm. Cartogr Geogr Inf Sci. 45(3):255–269. doi:10.1080/15230406.2017.1324823.
  • Chen J, Qian H, Wang X, He H, Hu H. 2016. Dynamic simplification method to improve the matching rate of line features. Acta Geod Cartogr Sin. 45(4):486–493.
  • Duan P, Qian H, He H, Liu C, Xie L. 2019. A lake selection method based on dynamic multi-scale clustering. Geom Inf Sci Wuhan Univ. 44(10):1567–1574.
  • Duan P, Qian H, He H, Xie L, Luo D. 2020. A line simplification method based on support vector machine. Geom Inf Sci Wuhan Univ. 45(5):744–752.
  • Fu Z, Yang Y, Gao X, Zhao X, Fan L. 2016. A multi-feature matching optimization algorithm for road networks. Acta Geod Cartogr Sin. 45(5):608–615.
  • Gao X, Yan H, Lu X. 2022. A semantic similarity calculation method for multi-scale map spatial residential areas. Acta Geod Cartogr Sin. 51(1):95–103.
  • Guo W, Liu H, Sun Q, Yu A, Ding Z. 2019. A multi-source contour matching method considering geometric feature similarity. Acta Geod Cartogr Sin. 48(5):643–653.
  • Kim JO, Yu K, Heo J, Lee WH. 2010. A new method for matching objects in two different geospatial datasets based on the geographic context. Comput Geosci. 36(9):1115–1122. doi:10.1016/j.cageo.2010.04.003.
  • Lei TL. 2021. Large scale geospatial data conflation: a feature matching framework based on optimization and divide-and-conquer. Comput Environ Urban Syst. 87:101618. doi:10.1016/j.compenvurbsys.2021.101618.
  • Li L, Goodchild MF. 2011. An optimisation model for linear feature matching in geographical data conflation. Int J Image Data Fusion. 2(4):309–328. doi:10.1080/19479832.2011.577458.
  • Liu C, Wu F, Gong X, Xing R, Du J. 2021a. A clustering method for natural surface clustering degree based on spatial knowledge mining. Acta Geod Cartogr Sin. 50(4):544–555.
  • Liu C, Wu F, Gong X, Xing R, Du J. 2021b. Pattern recognition of complex distributed ditches. IJGI. 10(7):450. doi:10.3390/ijgi10070450.
  • Liu L, Ding X, Zhu X, Fan L, Gong J. 2020. An iterative approach based on contextual information for matching multi‐scale polygonal object datasets. Trans GIS. 24(4):1047–1072. doi:10.1111/tgis.12625.
  • Sandro S, Massimo R, Matteo Z. 2011. Pattern recognition and typification of ditches. In: Ruas A, editor. Advances in cartography and GIScience. Vol 1. Berlin, Heidelberg, Germany: Springer Berlin Heidelberg; p. 425–437.
  • Tong X, Liang D, Jin Y. 2014. A linear road object matching method for conflation based on optimization and logistic regression. Int J Geogr Inf Sci. 28(4):824–846. doi:10.1080/13658816.2013.876501.
  • Wang H, Liu Y, Li S, Liang B, He Z. 2023. A GNSS high sampling rate path incremental map matching method. Acta Geod Cartogr Sin. 52(2):329–340.
  • Wang M, Ai T, Yan X, Xiao Y. 2020. Recognition of road orthogonal grid pattern by graph convolutional network model. Geom Inf Sci Wuhan Univ. 45(12):1960–1969.
  • Wang W, Yan H, Lu X, He Y, Liu T, Li W, Li P, Xu F. 2023. Drainage pattern recognition method considering local basin shape based on graph neural network. Int J Digit Earth. 16(1):593–619. doi:10.1080/17538947.2023.2172224.
  • Wu H, Xu S, Huang S, Wang J, Yang X, Liu C, Zhang Y. 2022. Optimal road matching by relaxation to min-cost network flow. Int J Appl Earth Obs Geoinf. 114:103057. doi:10.1016/j.jag.2022.103057.
  • Yang M, Jiang C, Yan X, Ai T, Cao M, Chen W. 2022. Detecting interchanges in road networks using a graph convolutional network approach. Int J Geogr Inf Sci. 36(6):1119–1139. doi:10.1080/13658816.2021.2024195.
  • Yang M, Kong B, Dang R, Yan X. 2022. Classifying urban functional regions by integrating buildings and points-of-interest using a stacking ensemble method. Int J Appl Earth Obs Geoinf. 108:102753. doi:10.1016/j.jag.2022.102753.
  • Yu W, Liu M. 2023. An iterative framework with active learning to match segments in road networks. Cartogr Geogr Inf Sci. 50(4):333–350. doi:10.1080/15230406.2023.2190935.
  • Zhang J, Wang Y, Zhao W. 2018. An improved probabilistic relaxation method for matching multi-scale road networks. Int J Digit Earth. 11(6):635–655. doi:10.1080/17538947.2017.1341557.
  • Zhang M, Meng L. 2007. An iterative road-matching approach for the integration of postal data. Comput Environ Urban Syst. 31(5):597–615. doi:10.1016/j.compenvurbsys.2007.08.008.
  • Zhang WB, Ge Y, Leung Y, Zhou Y. 2021. A georeferenced graph model for geospatial data matching by optimising measures of similarity across multiple scales. Int J Geogr Inf Sci. 35(11):2339–2355. doi:10.1080/13658816.2020.1858301.
  • Zhang X, Ai T, Stoter J, Zhao X. 2014. Data matching of building polygons at multiple map scales improved by contextual information and relaxation. ISPRS J Photogramm. 92:147–163. doi:10.1016/j.isprsjprs.2014.03.010.
  • Zhang X, He X, Sun Y, Huang J, Zhang Z. 2022. Research status and prospects of multi-scale spatial data linkage update technology. Acta Geod Cartogr Sin. 51(7):1520–1535.
  • Zheng J, Gao Z, Ma J, Shen J, Zhang K. 2021. Deep graph convolutional networks for accurate automatic road network selection. IJGI. 10(11):768. doi:10.3390/ijgi10110768.
  • Zhong D. 2018. Research on line feature quality evaluation and multi-scale water system matching method. Wuhan, China: Wuhan University.