574
Views
5
CrossRef citations to date
0
Altmetric
Research Article

A point selection method in map generalization using graph convolutional network model

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 20-40 | Received 25 Oct 2022, Accepted 02 Mar 2023, Published online: 21 Mar 2023

References

  • Ai, T. (2021). Some thoughts on deep learning enabling cartography. Acta Geodaetica et Cartographica Sinica, 50(9), 1170. https://doi.org/10.11947/j.AGCS.2021.20210091
  • Ai, T., Ke, S., Yang, M., & Li, J. (2017). Envelope generation and simplification of polylines using Delaunay triangulation. International Journal of Geographical Information Science, 31(2), 297–319. https://doi.org/10.1080/13658816.2016.1197399
  • Ai, T., & Liu, Y. (2002). A method of point cluster simplification with spatial distribution properties preserved. Acta Geodaetica et Cartographica Sinica, 31(2), 175–181. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKgchrJ08w1e7lwLRIsgSA99_zn9zvdiCOGm1JYtloTsnLYz0S67-NhtxlBwkQtKKWOX5RLNeUZ-9&uniplatform=NZKPT
  • Ai, T., & Yang, W. (2016). The detection of transport land-use data using crowdsourcing taxi trajectory. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B8, 8. https://doi.org/10.5194/ISPRS-ARCHIVES-XLI-B8-785-2016
  • Ai, T., Yin, H., Shen, Y., Yang, M., & Wang, L. (2019). A formal model of neighborhood representation and applications in urban building aggregation supported by Delaunay triangulation. PloS One, 14(7), e0218877. https://doi.org/10.1371/journal.pone.0218877
  • Ai, T., Zhang, X., Zhou, Q., & Yang, M. (2015). A vector field model to handle the displacement of multiple conflicts in building generalization. International Journal of Geographical Information Science, 29(8), 1310–1331. https://doi.org/10.1080/13658816.2015.1019886
  • Bahari, M., Nejjar, I., & Alahi, A. (2021). Injecting knowledge in data-driven vehicle trajectory predictors. Transportation Research Part C: Emerging Technologies, 128, 103010. https://doi.org/10.1016/j.trc.2021.103010
  • Burghardt, D., Purves, R., & Edwardes, A. (2004). Techniques for on the-fly generalisation of thematic point data using hierarchical data structures. In Proceedings of the GIS research UK 12th annual conference (pp. 28–30). https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=00f23e3db2f18d7397ea85c0dcc1d697913107a6
  • Cheng, B., Liu, Q., Li, X., & Wang, Y. (2013). Building simplification using backpropagation neural networks: A combination of cartographers’ expertise and raster-based local perception. GIScience & Remote Sensing, 50(5), 527–542. https://doi.org/10.1080/15481603.2013.823748
  • Chen, J., Hu, Y., Li, Z., Zhao, R., & Meng, L. (2009). Selective omission of road features based on mesh density for automatic map generalization. International Journal of Geographical Information Science, 23(8), 1013–1032. https://doi.org/10.1080/13658810802070730
  • De Berg, M., Bose, P., Cheong, O., & Morin, P. (2004). On simplifying dot maps. Computational Geometry, 27(1), 43–62. https://doi.org/10.1016/j.comgeo.2003.07.005
  • Delaunay, B. (1934). Sur la sphère vide [On the empty sphere]. Bulletin of the academy of sciences of the USSR. Classe des Sciences Mathématiques et Naturelles, 8, 793–800. http://galiulin.narod.ru/delaunay_.pdf
  • Deng, H., Wu, F., & Qian, H. (2003). A model of point cluster selection based on genetic algorithms. Journal of Image and Graphics, 8(8), 970–976. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKgchrJ08w1e7ZCYsl4RS_3jJcqypVGdX0-pxZNyD5WPSZV9yWuzjveJj8dECtfWVhuhysiix9X9p&uniplatform=NZKPT
  • Du, J., Wu, F., Xing, R., Gong, X., & Yu, L. (2021). Segmentation and sampling method for complex polyline generalization based on a generative adversarial network. Geocarto International, 37(14), 4158–4180. https://doi.org/10.1080/10106049.2021.1878288
  • Du, J., Wu, F., Yin, J., Liu, C., & Gong, X. (2022). Polyline simplification based on the artificial neural network with constraints of generalization knowledge. Cartography and Geographic Information Science, 49(4), 313–337. https://doi.org/10.1080/15230406.2021.2013944
  • Du, J., Zhang, S., Wu, G., Moura, J. M., & Kar, S. (2017). Topology adaptive graph convolutional networks. arXiv Preprint. https://arxiv.org/abs/1710.10370
  • Feng, Y., Thiemann, F., & Sester, M. (2019). Learning cartographic building generalization with deep convolutional neural networks. ISPRS International Journal of Geo-Information, 8(6), 258. https://doi.org/10.3390/ijgi8060258
  • Fourier, J. B. J. (1822). Théorie Analytique de la Chaleur [The analytic theory of heat]. Firmin Didot Père et Fils. https://doi.org/10.1017/CBO9780511693229
  • Goodchild, M. F. (2018). A GIScience perspective on the uncertainty of context. Annals of the American Association of Geographers, 108(6), 1476–1481. https://doi.org/10.1080/24694452.2017.1416281
  • Halevy, A., Norvig, P., & Pereira, F. (2009). The unreasonable effectiveness of data. IEEE Intelligent Systems, 24(2), 8–12. https://doi.org/10.1109/MIS.2009.36
  • Hammond, D. K., Vandergheynst, P., & Gribonval, R. (2011). Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis, 30(2), 129–150. https://doi.org/10.1016/j.acha.2010.04.005
  • Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507. https://doi.org/10.1126/science.1127647
  • Kang, Y., Gao, S., & Roth, R. E. (2019). Transferring multiscale map styles using generative adversarial networks. International Journal of Cartography, 5(2–3), 115–141. https://doi.org/10.1080/23729333.2019.1615729
  • Karsznia, I., & Sielicka, K. (2020). When traditional selection fails: How to improve settlement selection for small-scale maps using machine learning. ISPRS International Journal of Geo-Information, 9(4), 230. https://doi.org/10.3390/ijgi9040230
  • Karsznia, I., & Weibel, R. (2018). Improving settlement selection for small-scale maps using data enrichment and machine learning. Cartography and Geographic Information Science, 45(2), 111–127. https://doi.org/10.1080/15230406.2016.1274237
  • Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv Preprint. https://arxiv.org/abs/1609.02907
  • Lalitha, V., & Latha, B. (2022). A review on remote sensing imagery augmentation using deep learning. Materials Today: Proceedings, 62(7), 4772–4778. https://doi.org/10.1016/j.matpr.2022.03.341
  • Langran, G. E., & Poiker, T. K. (1986). Integration of name selection and name placement. In Proceedings of the second international symposium on spatial data handling (pp. 50–64). International Geographical Union and International Cartographic Association. https://cir.nii.ac.jp/crid/1571980075586056064
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
  • Lee, J., Jang, H., Yang, J., & Yu, K. (2017). Machine learning classification of buildings for map generalization. ISPRS International Journal of Geo-Information, 6(10), 309. https://doi.org/10.3390/ijgi6100309
  • Li, S., Sheng, C., & Wang, J. (2019). An algorithm of cartographical generalization for point cluster features based on point density analysis and adaptive difference detection. Geography and Geo-Information Science, 35, 1–5. https://doi.org/10.3969/j.issn.1672-0504.2019.02.001
  • Liu, Y., Zou, X., Ma, S., Avdeev, M., & Shi, S. (2022). Feature selection method reducing correlations among features by embedding domain knowledge. Acta Materialia, 238, 118195. https://doi.org/10.1016/j.actamat.2022.118195
  • Li, C., Wu, W., & Yin, Y. (2018). Hierarchical elimination selection method of dendritic river network generalization. PloS One, 13(12), e0208101. https://doi.org/10.1371/journal.pone.0208101
  • Lu, Y., Du, J., & Zhai, J. (2001). A model of point cluster generalization with spatial distribution features recognized and measured. In Proceedings of 20th international cartographic conference. https://icaci.org/files/documents/ICC_proceedings/ICC2001/icc2001/file/f13009.pdf
  • Lu, X., Yan, H., Li, W., Li, X., & Wu, F. (2019). An algorithm based on the weighted network Voronoi diagram for point cluster simplification. ISPRS International Journal of Geo-Information, 8(3), 105. https://doi.org/10.3390/ijgi8030105
  • Lyu, Z., Sun, Q., Ma, J., Xu, Q., Li, Y., & Zhang, F. (2022). Road network generalization method constrained by residential areas. ISPRS International Journal of Geo-Information, 11(3), 159. https://doi.org/10.3390/ijgi11030159
  • Mandelbrot, B. B. (1982). The fractal geometry of nature. WH freeman. https://users.math.yale.edu/~bbm3/web_pdfs/encyclopediaBritannica.pdf
  • Perozzi, B., Al-Rfou, R., & Skiena, S. (2014). Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/2623330.2623732
  • Qian, H., Meng, L., & Zhang, M. (2007, August 4-10). Network simplification based on the algorithm of polarization transformation. In Proceedings of the XXIII International Cartographic Conference (ICC). Cartographic Generalization and Multiple Representation. https://docest.com/networksimplification-based-on-the-algorithm-of-polarization-transformation
  • Qin, Y., & Liu, B. (2022). KDM: A knowledge-guided and data-driven method for few-shot video action recognition. Neurocomputing, 510, 69–78. https://doi.org/10.1016/j.neucom.2022.09.011
  • Sadahiro, Y. (1997). Cluster perception in the distribution of point objects. Cartographica: The International Journal for Geographic Information and Geovisualization, 34(1), 49–62. https://doi.org/10.3138/Y308-2422-8615-1233
  • Schuster, D., van Zelst, S. J., & van der Aalst, W. M. (2022). Utilizing domain knowledge in data-driven process discovery: A literature review. Computers in Industry, 137, 103612. https://doi.org/10.1016/j.compind.2022.103612
  • Sester, M., Feng, Y., & Thiemann, F. (2018). Building generalization using deep learning. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4, 42, 565–572. https://doi.org/10.5194/ISPRS-ARCHIVES-XLII-4-565-2018
  • Stefan, P. (2013). Quadtree- and octree-based approach for point data selection in 2D or 3D. Annals of GIS, 19(1), 37–44. https://doi.org/10.1080/19475683.2012.758171
  • Steiniger, S., Burghardt, D., & Weibel, R. (2006). Recognition of island structures for map generalization. In Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems (pp. 67–74). https://doi.org/10.1145/1183471.1183484
  • Steiniger, S., & Weibel, R. (2007). Relations among map objects in cartographic generalization. Cartography and Geographic Information Science, 34(3), 175–197. https://doi.org/10.1559/152304007781697866
  • Steven, M. R. (2019). A network approach to the production of geographic context using exponential random graph models. International Journal of Geographical Information Science, 33(6), 1270–1288. https://doi.org/10.1080/13658816.2018.1563299
  • Thomson, R. C., & Brooks, R. (2001). Exploiting perceptual grouping for map analysis, understanding and generalization: The case of road and river networks. In Proceedings of GREC ’01, Lecture Notes in Computer Science, Springer. https://doi.org/10.1007/3-540-45868-9_12
  • Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(sup1), 234–240. https://doi.org/10.2307/143141
  • Töpfer, F., & Pillewizer, W. (1966). The principles of selection. The Cartographic Journal, 3(1), 10–16. https://doi.org/10.1179/caj.1966.3.1.10
  • Touya, G., Zhang, X., & Lokhat, I. (2019). Is deep learning the new agent for map generalization? International Journal of Cartography, 5(2–3), 142–157. https://doi.org/10.1080/23729333.2019.1613071
  • Van Kreveld, M., Van, O. R., & Snoeyink, J. (1997). Efficient settlement selection for interactive display. In Proceedings of the auto-carto 13 (pp. 287–296). American Congress on Surveying and Mapping. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=3364903fcbb95a66c30a64f44bf5e1d942ea8b07
  • Wang, M., Ai, T., Yan, X., & Xiao, Y. (2020). Grid pattern recognition in road networks based on graph convolution network model. Geomatics and Information Science of Wuhan University, 45(12), 1960–1969. https://doi.org/10.13203/j.whugis20200022
  • Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. https://doi.org/10.1109/TIP.2003.819861
  • Wang, B., Cheng, L., Sheng, J., Hou, Z., & Chang, Y. (2022). Graph convolutional networks fusing motif-structure information. Scientific Reports, 12(1), 1–12. https://doi.org/10.1038/s41598-022-13277-z
  • Wang, L., Guo, Q., Liu, Y., Sun, Y., & Wei, Z. (2017). Contextual building selection based on a genetic algorithm in map generalization. ISPRS International Journal of Geo-Information, 6(9), 271. https://doi.org/10.3390/ijgi6090271
  • Wang, Y., & Jing, C. (2022). Spatiotemporal graph convolutional network for multi-scale traffic forecasting. ISPRS International Journal of Geo-Information, 11(2), 102. https://doi.org/10.3390/ijgi11020102
  • Wang, Q., & Wu, H. (1996). The research on fractal method of automatic generalization of map polygons. Geomatics and Information Science of Wuhan University, 21(1), 59–63. http://ch.whu.edu.cn/en/article/id/4082
  • Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2020). Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys, 53(3), 1–34. https://doi.org/10.1145/3386252
  • Wan, Y., Yuan, C., Zhan, M., & Chen, L. (2022). Robust graph learning with graph convolutional network. Information Processing & Management, 59(3), 102916. https://doi.org/10.1016/j.ipm.2022.102916
  • Weibel, R. (1995). Map generalization in the context of digital systems. Cartography and Geographic Information Systems, 22(4), 259–263. https://doi.org/10.1559/152304095782540285
  • Wu, H. (1997). Principle of convex hull and its applications in generalization of grouped point objects. Engineering of Surveying and Mapping, 1, 1–6. https://en.cnki.com.cn/Article_en/CJFDTOTAL-CHGC199701000.htm
  • Wu, F., Deng, H., & Qian, H. (2003, August 10-16). A Point cluster selection model based on genetic algorithms. In Proceedings of the 21st International Cartographic Conference (ICC) (pp. 252–259). https://icaci.org/files/documents/ICC_proceedings/ICC2003/Papers/028.pdf
  • Xu, Y., Jin, S., Chen, Z., Xie, X., Hu, S., & Xie, Z. (2022). Application of a graph convolutional network with visual and semantic features to classify urban scenes. International Journal of Geographical Information Science, 36(10), 1–26. https://doi.org/10.1080/13658816.2022.2048834
  • Xu, X., Liu, W., & Yu, L. (2022). Trajectory prediction for heterogeneous traffic-agents using knowledge correction data-driven model. Information Sciences, 608, 375–391. https://doi.org/10.1016/j.ins.2022.06.073
  • Yan, X., Ai, T., Yang, M., & Yin, H. (2019). A graph convolutional neural network for classification of building patterns using spatial vector data. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 259–273. https://doi.org/10.1016/j.isprsjprs.2019.02.010
  • Yang, W., Ai, T., & Lu, W. (2018). A method for extracting road boundary information from crowdsourcing vehicle GPS trajectories. Sensors, 18(4), 1261. https://doi.org/10.3390/s18041261
  • 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. International Journal of Applied Earth Observation and Geoinformation, 108, 102753. https://doi.org/10.1016/j.jag.2022.102753
  • 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. International Journal of Geographical Information Science, 36(2), 280–309. https://doi.org/10.1080/13658816.2021.1873998
  • Yan, H., & Weibel, R. (2008). An algorithm for point cluster generalization based on the Voronoi diagram. Computers & Geosciences, 34(8), 939–954. https://doi.org/10.1016/j.cageo.2007.07.008
  • Yu, H., Ai, T., Yang, M., Huang, L., & Gao, A. (2022a). Automatic segmentation of parallel drainage patterns supported by a graph convolution neural network. Expert Systems with Applications, 211, 118639. https://doi.org/10.1016/j.eswa.2022.118639
  • Yu, H., Ai, T., Yang, M., Huang, L., & Yuan, J. (2020b). A recognition method for drainage patterns using a graph convolutional network. International Journal of Applied Earth Observation and Geoinformation, 107, 102696. https://doi.org/10.1016/j.jag.2022.102696
  • Yu, W., & Chen, Y. (2022). Data-driven polyline simplification using a stacked autoencoder-based deep neural network. Transactions in GIS, 26, 2302–2325. https://doi.org/10.1111/tgis.12965
  • Zhang, Y., Cheng, T., Ren, Y., & Xie, K. (2020). A novel residual graph convolution deep learning model for short-term network-based traffic forecasting. International Journal of Geographical Information Science, 34(5), 969–995. https://doi.org/10.1080/13658816.2019.1697879
  • Zhang, L., Song, H., Aletras, N., & Lu, H. (2022). Node-feature convolution for graph convolutional networks. Pattern Recognition, 128, 108661. https://doi.org/10.1016/j.patcog.2022.108661
  • Zhao, R., Ai, T., Yu, W., He, Y., & Shen, Y. (2020). Recognition of building group patterns using graph convolutional network. Cartography and Geographic Information Science, 47(5), 400–417. https://doi.org/10.1080/15230406.2020.1757512
  • Zhu, Y., Ma, J., Yuan, C., & Zhu, X. (2022). Interpretable learning based dynamic graph convolutional networks for alzheimer’s disease analysis. Information Fusion, 77, 53–61. https://doi.org/10.1016/j.inffus.2021.07.013

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.