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However, the previous selection methods could not simultaneously consider both attribute characteristics and spatial structure. In light of this, an intelligent road network selection method based on a graph neural network (GNN) is proposed in this paper. Firstly, the selection case is designed to construct a sample library. Secondly, some neighbor sampling and aggregation rules are developed to update road features. Then, a GNN-based selection model is designed to calculate classification labels, thus completing road network selection. Finally, a few comparative analyses with different selection methods are conducted, verifying that most of the accuracy values of the GNN model are stable over 90%. The experiments indicate that this method could aggregate stroke nodes and their neighbors together to synchronously preserve semantic, geometric, and topological features of road strokes, and the selection result is closer to the reference map. Therefore, this paper could bridge the distance between deep learning and cartographic generalization, thus facilitating a more intelligent road network selection method.<\/jats:p>","DOI":"10.3390\/ijgi12080336","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T12:10:30Z","timestamp":1691755830000},"page":"336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Method for Intelligent Road Network Selection Based on Graph Neural Network"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6678-4168","authenticated-orcid":false,"given":"Xuan","family":"Guo","sequence":"first","affiliation":[{"name":"Institute of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"},{"name":"State Key Laboratory of Geo-Information Engineering, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3211-5342","authenticated-orcid":false,"given":"Junnan","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geo-Information Engineering, Xi\u2019an 710054, China"},{"name":"Institute of Earth Science and Technology, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Earth Science and Technology, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haizhong","family":"Qian","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1559\/152304095782540267","article-title":"Analysis of urban road networks to support cartographic generalization","volume":"22","author":"Mackaness","year":"1995","journal-title":"Cartogr. Geogr. Inf. Syst."},{"key":"ref_2","unstructured":"Edwardes, A.J., and Mackaness, W.A. (2000, January 5\u20137). Intelligent generalization of urban road networks. Proceedings of the GISRUK 2000 Conference, York, UK."},{"key":"ref_3","unstructured":"Mackaness, W.A., Ruas, A., and Sarjakoski, L.T. (2011). Generalisation of Geographic Information: Cartographic Modelling and Applications, Elsevier."},{"key":"ref_4","unstructured":"Thomson, R.C., and Richardson, D.E. (1999, January 14\u201321). The \u2018good continuation\u2019principle of perceptual organization applied to the generalization of road networks. Proceedings of the 19th International Cartographic Conference, Ottawa, ON, Canada."},{"key":"ref_5","first-page":"75","article-title":"Integrating Thematic, Geometric, and Topological Information in the Generalization of Road Networks","volume":"33","author":"Richardson","year":"1996","journal-title":"Cartogr. Int. J. Geogr. Inf. Geovisualization"},{"key":"ref_6","unstructured":"Hamilton, W., Ying, Z., and Leskovec, J. (2017). Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Li, L., Chu, Y., Yang, Z., Wang, Z., and Shan, W. (2022). Efficient Supervised Image Clustering Based on Density Division and Graph Neural Networks. Remote Sens., 14.","DOI":"10.3390\/rs14153768"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3505","DOI":"10.1080\/10298436.2021.1904237","article-title":"An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil","volume":"23","author":"Raja","year":"2022","journal-title":"Int. J. Pavement Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105364","DOI":"10.1016\/j.cageo.2023.105364","article-title":"Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling","volume":"176","author":"Dahal","year":"2023","journal-title":"Comput. Geosci."},{"key":"ref_10","first-page":"4402","article-title":"GSNet: Learning spatial-temporal correlations from geographical and semantic aspects for traffic accident risk forecasting","volume":"35","author":"Wang","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1080\/13658810802070730","article-title":"Selective omission of road features based on mesh density for automatic map generalization","volume":"23","author":"Chen","year":"2009","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1080\/13658816.2011.609990","article-title":"A comparative study of various strategies to concatenate road segments into strokes for map generalization","volume":"26","author":"Zhou","year":"2012","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1080\/15230406.2014.928482","article-title":"Road network selection for medium scales using an extended stroke-mesh combination algorithm","volume":"41","author":"Benz","year":"2014","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_14","first-page":"210","article-title":"Use of graph theory to support map generalization","volume":"20","author":"Mackaness","year":"1993","journal-title":"Cartogr. Geogr. Inf. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1023\/B:GEIN.0000017746.44824.70","article-title":"A structural approach to the model generalization of an urban street network","volume":"8","author":"Jiang","year":"2004","journal-title":"GeoInformatica"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1111\/j.1467-9671.2004.00186.x","article-title":"Selection of streets from a network using self-organizing maps","volume":"8","author":"Jiang","year":"2004","journal-title":"Trans. GIS"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1080\/13658816.2019.1650936","article-title":"Road network generalization considering traffic flow patterns","volume":"34","author":"Yu","year":"2019","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1111\/tgis.12100","article-title":"Mapping large spatial flow data with hierarchical clustering","volume":"18","author":"Zhu","year":"2014","journal-title":"Trans. GIS"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Han, Y., Wang, Z., Lu, X., and Hu, B. (2020). Application of AHP to Road Selection. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9020086"},{"key":"ref_20","first-page":"164","article-title":"A Generalization Model of Road Networks Based on Genetic Algorithm","volume":"31","author":"Deng","year":"2006","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Guo, M., Qian, H., Wang, X., He, H., and Huang, Z. (2013, January 20\u201322). A new road network selection approach based on the importance criteria of spatial interactive relationship. Proceedings of the 2013 21st International Conference on Geoinformatics, Kaifeng, China.","DOI":"10.1109\/Geoinformatics.2013.6626168"},{"key":"ref_22","first-page":"761","article-title":"Intelligent Road-network Selection Using Cases Based Reasoning","volume":"43","author":"Guo","year":"2014","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1179\/1743277413Y.0000000042","article-title":"Use of artificial neural networks for selective omission in updating road networks","volume":"51","author":"Zhou","year":"2014","journal-title":"Cartogr. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1179\/1743277414Y.0000000083","article-title":"A comparative study of various supervised learning approaches to selective omission in a road network","volume":"54","author":"Zhou","year":"2017","journal-title":"Cartogr. J."},{"key":"ref_25","unstructured":"Liu, K. (2017). Research on Intelligent Selection of Road Network Automatic Generalization Based on Kernel-based Machine Learning. [Master\u2019s thesis, Nanjing University]."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cao, X., Zhang, K., and Jiao, L. (2022). CSANet: Cross-Scale Axial Attention Network for Road Segmentation. Remote Sens., 15.","DOI":"10.3390\/rs15010003"},{"key":"ref_27","unstructured":"Yuan, L., Liu, K., Liu, P., Shen, J., and Jingsong, M.A. (2019). Small scale road network selection using radial basis function neural network. Sci. Surv. Mapp., 44."},{"key":"ref_28","unstructured":"Li, J. (2014). Study on the Road Network Selection Methods Based on the BP Neural Network. [Master\u2019s thesis, Nanjing University]."},{"key":"ref_29","unstructured":"Yang, M. (2013). Research on the Self-Organizing Map\u2019s Application in the Cartographic Generalization of Road Networks. [Master\u2019s thesis, Nanjing University]."},{"key":"ref_30","unstructured":"Zhang, K., Zheng, J., Shen, J., and Ma, J. (2021). Application of the graph convolution network in the selection of road network. Sci. Surv. Mapp., 46."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Riesbeck, C.K., and Schank, R.C. (2013). Inside Case-Based Reasoning, Psychology Press.","DOI":"10.4324\/9780203781821"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1179\/caj.1996.33.1.5","article-title":"A Dynantic Decision Tree Structure Supporting Urban Road Network Automated Generalization","volume":"33","author":"Peng","year":"2013","journal-title":"Cartogr. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1179\/caj.2002.39.2.153","article-title":"Topographic map generalization: Association of road elimination with thematic attributes","volume":"39","author":"Li","year":"2002","journal-title":"Cartogr. J."},{"key":"ref_34","unstructured":"Zhang, Q. (2005). Developments in Spatial Data Handling, Springer."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.compenvurbsys.2007.03.003","article-title":"Experiential hierarchies of streets","volume":"32","author":"Tomko","year":"2008","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1111\/j.1467-9671.2010.01215.x","article-title":"A Road Network Selection Process Based on Data Enrichment and Structure Detection","volume":"14","author":"Touya","year":"2010","journal-title":"Trans. GIS"},{"key":"ref_37","first-page":"S194","article-title":"Road selection based on Voronoi diagrams and \u201cstrokes\u201d in map generalization","volume":"12","author":"Liu","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","unstructured":"Zhou, Q., and Li, Z. (2011). Advances in Cartography and GIScience. Volume 1, Springer."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1109\/TITS.2020.3026025","article-title":"GraphSAGE-Based Traffic Speed Forecasting for Segment Network with Sparse Data","volume":"23","author":"Liu","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.isprsjprs.2019.02.010","article-title":"A graph convolutional neural network for classification of building patterns using spatial vector data","volume":"150","author":"Yan","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1080\/13658816.2020.1768260","article-title":"Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps","volume":"35","author":"Yan","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zheng, J., Gao, Z., Ma, J., Shen, J., and Zhang, K. (2021). Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10110768"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/8\/336\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:31:55Z","timestamp":1760128315000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/8\/336"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,11]]},"references-count":42,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["ijgi12080336"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12080336","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,11]]}}}