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Research Article

Determining the optimal generalization operators for building footprints using an improved graph neural network model

ORCID Icon, , ORCID Icon, ORCID Icon & ORCID Icon
Article: 2306265 | Received 10 Oct 2023, Accepted 11 Jan 2024, Published online: 30 Jan 2024

References

  • Abbaspour RA, Chehreghan A, Chamani M. 2021. Multi-scale polygons matching using a new geographic context descriptor. Appl Geomat. 13(4):885–899. doi:10.1007/s12518-021-00396-x.
  • Abhilash B, Busari E, Syranidou C, Linssen J, Stolten D. 2022. Classification of building types in Germany: a data-driven modeling approach. Data. 7(4):45. doi:10.3390/data7040045.
  • Ai T, Cheng X, Liu P, Yang M. 2013. A shape analysis and template matching of building features by the Fourier Transform Method. Comp Environ Urban Syst. 41:219–233. doi:10.1016/j.compenvurbsys.2013.07.002.
  • Ai T. 2021. Some thoughts on deep learning enabling cartography. Acta Geodaetica et Cartograhica Sinica. 50:1170–1182. doi:10.11947/j.AGCS.2021.20210091.
  • Altmann A, Toloşi L, Sander O, Lengauer T. 2010. Permutation importance: a corrected feature importance measure. Bioinformatics. 26(10):1340–1347. doi:10.1093/bioinformatics/btq134.
  • An X, Zhu Y, Yan X. 2023. Building selection method supported by convolutional neural network. Acta Geodaetica et Cartographica Sinica. 52:1574–1583. doi:10.11947/j.AGCS.2023.20220216.
  • Balboa JL, Ariza-López FJ. 2008. Generalization-oriented road line classification by means of an artificial neural network. GeoInformatica. 12:289–312. doi:10.1007/s10707-007-0026-z.
  • Basaraner M, Cetinkaya S. 2017. Performance of shape indices and classification schemes for characterising perceptual shape complexity of building footprints in GIS. International Journal of Geographical Information Science. 31(10):1952–1977. doi:10.1080/13658816.2017.1346257.
  • Bouvrie JV. 2006. Notes on convolutional neural networks. Cambridge, MA: Massachusetts Institute of Technology.
  • Chen J, Chen H. 2021. Edge-featured graph attention network. arXiv. preprint arXiv210107671 doi:10.48550/arXiv.2101.07671.
  • Cheng B, Liu Q, Li X. 2015. Local perception-based intelligent building outline aggregation approach with back propagation neural network. Neural Process Lett. 41(2):273–292. doi:10.1007/s11063-014-9345-x.
  • Courtial A, Ayedi AE, Touya G, Zhang X. 2020. Exploring the Potential of deep learning segmentation for mountain roads generalisation. IJGI. 9(5):338. doi:10.3390/ijgi9050338.
  • Courtial A, Touya G, Zhang X. 2021a. Can graph convolution networks learn spatial relations? Abstr Int Cartogr Assoc. 3:1–2. doi:10.5194/ica-abs-3-60-2021.
  • Courtial A, Touya G, Zhang X. 2021b. Generative adversative networks to generalize urban areas in topographic maps. In: XXIV ISPRS Congress (2021 Edition). p. 15–22. nice (en ligne), france. doi:10.5194/isprs-archives-XLIII-B4-2021-15-2021. Hal-03306595
  • Courtial A, Touya G, Zhang X. 2022. Representing vector geographic information as a tensor for deep learning based map generalisation. AGILE GIScience Ser. 3:1–8. doi:10.5194/agile-giss-3-32-2022.
  • Deng M, Tang J, Liu Q, Wu F. 2018. Recognizing building groups for generalization: a comparative study. Cartogr Geogr Inform Sci. 45(3):187–204. doi:10.1080/15230406.2017.1302821.
  • Duchêne C, Touya G, Taillandier P, Gaffuri J, Ruas A, Renard J. 2018. Multi-agents systems for cartographic generalization: feedback from past and on-going research. PhD dissertation, Institut National de l’Information Géographique et Forestière doi:10.13140/RG.2.2.35489.92006.
  • Feng Y, Thiemann F, Sester M. 2019. Learning cartographic building generalization with deep convolutional neural networks. IJGI. 8(6):258. doi:10.3390/ijgi8060258.
  • Fiedukowicz A. 2020. The role of spatial context information in the generalization of geographic information: using reducts to indicate relevant attributes. IJGI. 9(1):37. doi:10.3390/ijgi9010037.
  • Galkin F, Aliper A, Kuznetsov I, Gladyshev VN, Zhavoronkov A. 2018. Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects. bioRxiv. doi:10.1016/j.isci.2020.101199.
  • Gong L, Cheng Q. 2019. Exploiting edge features for graph neural networks. arXiv. Preprint. arXiv1809.02709: 9203–9211 doi:10.48550/arXiv.1809.02709.
  • Hu Y, Liu C, Li Z, Xu J, Han Z, Guo J. 2022. Few-shot building footprint shape classification with relation network. IJGI. 11(5):311. doi:10.3390/ijgi11050311.
  • Karsznia I, Sielicka K. 2020. When traditional selection fails: how to improve settlement selection for small-scale maps using machine learning. IJGI. 9(4):230. doi:10.3390/ijgi9040230.
  • Karsznia I, Wereszczyńska K, Weibel R. 2022. Make it simple: effective road selection for small-scale map design using decision-tree-based models. IJGI. 11(8):457. doi:10.3390/ijgi11080457.
  • Kipf T, Welling M. 2017. Semi-supervised classification with graph convolutional networks. arXiv. preprint. arXiv1609.02907. doi:10.48550/arXiv.1609.02907.
  • Lamy S, Ruas A, Demazeu Y, Jackson M, Mackaness WA, Weibel R. 1999. The application of agents in automated map generalization. In: Proceedings of the 19th Ica Meeting. Ottawa, Canada 14, p. 160–169.
  • Lee J, Jang H, Yang J, Yu K. 2017. Machine learning classification of buildings for map generalization. IJGI. 6(10):309. doi:10.3390/ijgi6100309.
  • Li C, Wu W, Yin Y, Wu P, Wu Z. 2022. A multi‐scale partitioning and aggregation method for large volumes of buildings considering road networks association constraints. Transat GIS. 26(2):779–798. doi:10.1111/tgis.12885.
  • Li Y, Lu X, Yan H, Wang W, Li P. 2022. A skeleton-line-based graph convolutional neural network for areal settlements: shape classification. Appl Sci. 12(19):10001. doi:10.3390/app121910001.
  • Li Z, Yan HW, Ai T, Chen J. 2004. Automated building generalization based on urban morphology and gestalt theory. Inter J Geogr Inform Sci. 18(5):513–534. doi:10.1080/13658810410001702021.
  • Li Z. 2007. Digital map generalization at The Age of Enlightenment: a review of the first forty years. The Cartographic Journal. 44(1):80–93. doi:10.1179/000870407X173913.
  • Lin T, Goyal P, Girshick R, He K, Dollár P, Facebook AI, Research FAIR. 2017. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell. 42(2):318–327. doi:10.1109/TPAMI.2018.2858826.
  • Liqiang Z, Hao D, Dong C, Zhen W. 2013. A spatial cognition-based urban building clustering approach and its applications. Inter J Geogr Inform Sci. 27(4):721–740. doi:10.1080/13658816.2012.700518.
  • Liu C, Qian H, Wang X, He H, Xie L, Wang C. 2016. Linkage matching method of road and residential land using urban skeleton line network. c. 45:1485–1494. doi:10.11947/j.AGCS.2016.20160221.
  • Liu C, Qian H, He H, Wang X, Xie L. 2016. A Construction Method of Road and Residence Correlation Based on Urban Skeleton Network. In: Yuan H, Geng J, Bian F, editors. geo-spatial knowledge and intelligence. GRMSE 2016. Communications in computer and information science, 699. Singapore: Springer.
  • Liu C, Zhai R, Qian H, Gong X, Wang A, Wu F. 2023. Identification of drainage patterns using a graph convolutional neural network. Transactions in GIS. 27(3):752–776. doi:10.1111/tgis.13041.
  • Lyu Z, Sun Q, Ma J, Xu Q, Li Y, Zhang F. 2022. Road network generalization method constrained by residential areas. IJGI. 11(3):159. doi:10.3390/ijgi11030159.
  • MacEachren AM. 1985. Compactness of geographic shape: comparison and Evaluation of measures. Geografiska Annaler: Series B, Human Geography. 67(1):53–67. doi:10.2307/490799.
  • Peura M, Iivarinen J. 1997. Efficiency of simple shape descriptors. In: Cordella LP, Arcelli C, Sanniti di Baja G, editors. Advances in Visual Form Analysis: proceedings of the 3rd International Workshop on Visual Form. Singapore: World Scientific. p. 443–451.
  • Rieger MK, Coulson MR. 1993. Consensus or confusion: cartographers’ knowledge of generalization. Cartographica. 30(2-3):69–80. doi:10.3138/M6H4-1006-6422-H744.
  • Rosin PL. 2000. Measuring shape: ellipticity, rectangularity, and triangularity. Machine Vision and Applications. 14(3):172–184. doi:10.1007/s00138-002-0118-6.
  • Ruas A, Duchene C. 2007. A prototype generalisation system based on the multi-agent system paradigm. In: Mackaness WA, Ruas A, Sarjakoski LT, editors. Generalisation of Geographic Information. Amsterdam: Elsevier. p. 269–284.
  • Sester M, Feng Y, Thiemann F. 2018. Building generalization using deep learning. Int Arch Photogramm Remote Sens Spatial Inf Sci. XLII-4:565–572. doi:10.5194/isprs-archives-XLII-4-565-2018.
  • Shea KS, Master RB. 1989. Cartographic generalization in a digital environment: when and how to generalize. In: International Symposium on Computer-Assisted Cartography. p. 56–67.
  • Shuai Y, Shuai H, Ni L. 2007. Polygon cluster pattern recognition based on new visual distance. In: Proceedings Volume 6753, Geoinformatics 2007: Geospatial Information Science, Nanjing, China; 675316. doi:10.1117/12.761778.
  • Smilkov D, Thorat N, Kim B, Viégas F, Wattenberg M. 2017. SmoothGrad: removing noise by adding noise. ArXiv. Preprints. ArXiv abs/1706.03825. doi:10.48550/arXiv.1706.03825.
  • Steiniger S, Taillandier P, Weibel R. 2010. Utilising urban context recognition and machine learning to improve the generalisation of buildings. Inter J Geogr Inform Sci. 24(2):253–282. doi:10.1080/13658810902798099.
  • Susetyo DB, Hidayat F. 2019. Specification of map generalization from large scale to small scale based on existing data. IOP Conf Ser Earth Environ Sci. 280(1):012026. doi:10.1088/1755-1315/280/1/012026.
  • Touya G, Zhang X, Lokhat I. 2019. Is deep learning the new agent for map generalization? Inter J Cartogr. 5(2-3):142–157. doi:10.1080/23729333.2019.1613071.
  • Touya G. 2021. Multi-criteria geographic analysis for automated cartographic generalization. The Cartographic Journal. 59(1):18–34. doi:10.1080/00087041.2020.1858608.
  • Touya G, Lokhat I. 2020. Deep learning for enrichment of vector spatial databases. ACM Trans Spatial Algorithms Syst. 6(3):1–21. doi:10.1145/3382080.
  • Touya G, Duchêne C, Ruas A. 2010. Collaborative generalisation: formalisation of generalisation knowledge to orchestrate different cartographic generalisation processes. In: Geographic Information Science: 6th International Conference, GIScience 2010, Zurich, Switzerland, September 14–17, 2010. Proceedings 6, Springer, Berlin. p. 264–278.
  • Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. 2018. Graph attention networks. ArXiv. preprint. ArXiv abs/1710.10903. doi:10.48550/arXiv.1710.10903.
  • Wang W, Du S, Guo Z, Luo L. 2015. Polygonal clustering analysis using multilevel graph‐partition. Transactions in GIS. 19(5):716–736. doi:10.1111/tgis.12124.
  • Wang X, Qian H, He H, Chen J, Hu H. 2015. Matching multi-source residential land by blank area skeleton line mesh. Acta Geodaetica et Cartographica Sinica. 44:927–935. doi:10.11947/j.AGCS.2015.20140462.
  • Wang Z, Chen J, Chen H. 2021. EGAT: edge-featured graph attention network. In: Farkaš I, Masulli P, Otte S, Wermter S, editors. Artificial neural networks and machine learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science. Cham: Springer International Publishing. p. 12891.
  • Ware JM, Jones CB, Thomas N. 2003. Automated map generalization with multiple operators: a simulated annealing approach. Inter J Geograph Inform Sci. 17(8):743–769. doi:10.1080/13658810310001596085.
  • Wu F, Du J, Qian H, Zhai R. 2022. The development and thinking of map comprehensive intelligence research. Wuhan Daxue Xuebao. 47:1675–1687.
  • Xie L, Qian H, He H, Liu C, Duan P. 2017. Settlement selection method based on case reasoning. Acta Geodaetica et Cartographica Sinica. 46:1910–1918. doi:10.11947/j.AGCS.2017.20170061.
  • Xu K, Hu W, Leskovec J, Jegelka S. 2018. How powerful are graph neural networks? ArXiv. preprints. ArXiv abs/1810.00826. doi:10.48550/arXiv.1810.00826.
  • Yan H,Weibel R,Yang B. 2008. A multi-parameter approach to automated building grouping and generalization. Geoinformatica. 12(1):73–89. doi:10.1007/s10707-007-0020-5.
  • Yan X, Ai T, Yang M, Yin H. 2019. A Graph convolutional neural network for classification of building patterns using spatial vector data. ISPRS J Photogr Remote Sens. 150:259–273. doi:10.1016/j.isprsjprs.2019.02.010.
  • Yan X, Ai T, Yang M, Tong X, Liu Q. 2020. A graph deep learning approach for urban building grouping. Geocarto Inter. 37(10):2944–2966. doi:10.1080/10106049.2020.1856195.
  • Yan X, Chen H, Huang H, Liu Q, Yang M. 2021. Building typification in map generalization using affinity propagation clustering. IJGI. 10(11):732. doi:10.3390/ijgi10110732.
  • Yan X, Yuan T, Yang M. 2022. Building shape characterization representation and adaptive simplification methods. Acta Geodaetica et Cartographica Sinica. 51:269–278. doi:10.11947/j.AGCS.2022.20210302.
  • Yang M, Yuan T, Yan X, Ai T, Jiang C. 2022. A hybrid approach to building simplification with an evaluator from a backpropagation neural network. Inter J Geograph Inform Sci. 36(2):280–309. doi:10.1080/13658816.2021.1873998.
  • Yang Y, Li D. 2020. NENN: incorporate Node and Edge Features in Graph Neural Networks. In: Proceedings of the 12th Asian Conference on Machine Learning, PMLR129, p. 593–608.
  • Zaremba W, Sutskever I, Vinyals O. 2014. Recurrent neural network regularization. ArXiv. Preprints. ArXiv abs/1409.2329. doi:10.48550/arXiv.1409.2329.
  • Zhang X, Ai T, Stoter J. 2008. The Evaluation of Spatial Distribution Density in Map Generalization. In: The XXI Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS 2008) p. 181–188.
  • Zhang X, Ai T, Stoter J, Kraak M-J, Molenaar M. 2011. Building pattern recognition in topographic data: examples on collinear and curvilinear alignments. GeoInformatica. 17(1):1–33. doi:10.1007/s10707-011-0146-3.
  • Zhang X, Yin W, Yang M, Ai T, Stoter J. 2018. Updating authoritative spatial data from timely sources: a multiple representation approach. Int J Appl Earth Obs Geoinf. 72:42–56. doi:10.1016/j.jag.2018.05.022.
  • Zhang Z, Liu T, Du P, Yang G. 2022. DGCNN recognition method of spatial map convolutional model of typical building group pattern. Wuhan Daxue Xuebao. 16:11–13. 1–
  • Zhao L, Akoglu L. 2019. PairNorm: tackling Oversmoothing in GNNs. ArXiv. preprints. arXiv1909.12223. doi:10.48550/arXiv.1909.12223.
  • Zhou S, Regnauld N, Roensdorf C. 2009. Generalisation log for managing and utilising a multi-representation spatial database in map production. Comput Environ Urban Syst. 33(5):334–348. doi:10.1016/j.compenvurbsys.2009.06.004.