Review of road selection methods for the purpose of multiscale mapping

Adolf, A & Karsznia, I 2024, ‘Effective visualization of the AI-based road selection results for small scale maps design’, Abstracts of the International Cartographic Association, vol. 8, article numer: 1. AdolfA KarszniaI 2024 ‘Effective visualization of the AI-based road selection results for small scale maps design’ Abstracts of the International Cartographic Association 8 article numer: 1. Search in Google Scholar

Bellman, RE 1978, An introduction to artificial intelligence: Can computers think?, Boyd & Fraser Publishing Company, San Francisco. BellmanRE 1978 An introduction to artificial intelligence: Can computers think? Boyd & Fraser Publishing Company San Francisco Search in Google Scholar

Benz, SA & Weibel, R 2014, ‘Road network selection for medium scales using an extended stroke-mesh combination algorithm’, Cartography and Geographic Information Science, vol. 41, no. 4, pp. 323–339. BenzSA WeibelR 2014 ‘Road network selection for medium scales using an extended stroke-mesh combination algorithm’ Cartography and Geographic Information Science 41 4 323 339 Search in Google Scholar

Bertin, J 1983, Semiology of graphics: Diagrams, networks, maps, University of Wisconsin Press, Madison. BertinJ 1983 Semiology of graphics: Diagrams, networks, maps University of Wisconsin Press Madison Search in Google Scholar

Brassel, K & Weibel, R 1988, ‘A review and conceptual framework of automated map generalization’, International Journal of Geographical Information Systems, vol. 2, no. 3, pp. 229–244. BrasselK WeibelR 1988 ‘A review and conceptual framework of automated map generalization’ International Journal of Geographical Information Systems 2 3 229 244 Search in Google Scholar

Brewer, CA, Stanislawski, LV, Buttenfield, BP, Syus, KA, McGilloway, J & Howard, MA 2013, ‘Automated thinning of road networks and road labels for multiscale design of The National Map of the United States’, Cartography and Geographic Information Science, vol. 40, no. 4, pp. 259–270. BrewerCA StanislawskiLV ButtenfieldBP SyusKA McGillowayJ HowardMA 2013 ‘Automated thinning of road networks and road labels for multiscale design of The National Map of the United States’ Cartography and Geographic Information Science 40 4 259 270 Search in Google Scholar

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, vol. 23, no. 8, pp.1013–1032. ChenJ HuY LiZ ZhaoR MengL 2009 ‘Selective omission of road features based on mesh density for automatic map generalization’ International Journal of Geographical Information Science 23 8 1013 1032 Search in Google Scholar

Chimani, M, Dijk, TC & Haunert, J 2014, ‘How to eat a graph: Computing selection sequences for the continuous generalization of road networks’ in Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ChimaniM DijkTC HaunertJ 2014 ‘How to eat a graph: Computing selection sequences for the continuous generalization of road networks’ in Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems Search in Google Scholar

Courtial, A, Ayedi, AE, Touya, G & Zhang, X 2020, ‘Exploring the potential of deep learning segmentation for mountain roads generalisation’, ISPRS International Journal of Geo-Information, vol. 9, no. 5. CourtialA AyediAE TouyaG ZhangX 2020 ‘Exploring the potential of deep learning segmentation for mountain roads generalisation’ ISPRS International Journal of Geo-Information 9 5 Search in Google Scholar

Courtial, A, Touya, G & Zhang, X 2022, ‘Constraint-based evaluation of map images generalized by deep learning’, Journal of Geovisualization and Spatial Analysis, vol. 6, no. 1. CourtialA TouyaG ZhangX 2022 ‘Constraint-based evaluation of map images generalized by deep learning’ Journal of Geovisualization and Spatial Analysis 6 1 Search in Google Scholar

Courtial, A, Touya, G & Zhang, X 2023, ‘Deriving map images of generalised mountain roads with generative adversarial networks’, International Journal of Geographical Information Science, vol. 36, no. 1, pp. 1–30. CourtialA TouyaG ZhangX 2023 ‘Deriving map images of generalised mountain roads with generative adversarial networks’ International Journal of Geographical Information Science 36 1 1 30 Search in Google Scholar

Du, J, Wu, F, Xing, R, Gong, X & Yu, L 2022, ‘Segmentation and sampling method for complex polyline generalization based on a generative adversarial network’, Geocarto International, vol. 37, no. 5, pp. 4158–4180. DuJ WuF XingR GongX YuL 2022 ‘Segmentation and sampling method for complex polyline generalization based on a generative adversarial network’ Geocarto International 37 5 4158 4180 Search in Google Scholar

Duchene, C, Baelley, B, Brewer, C, Burghardt, D, Buttenfield, B, Gaffuri, J, Kauferle, D, Lecordix, F, Maugeais, E, Nijhuis, R, Pla, M, Post, M, Regnauld, N, Stanislawski, L, Stoter, JE, Toth, K, Urbanke, S, van Altena, V & Wiedemann, A 2014, ‘Abstracting geographic information in a data rich world: Methodologies and applications of map generalisation’ in Generalisation in practice within national mapping agencies, eds D Burghardt, W Mackaness & C Duchene, Springer, Cham, p. 329–391. DucheneC BaelleyB BrewerC BurghardtD ButtenfieldB GaffuriJ KauferleD LecordixF MaugeaisE NijhuisR PlaM PostM RegnauldN StanislawskiL StoterJE TothK UrbankeS van AltenaV WiedemannA 2014 ‘Abstracting geographic information in a data rich world: Methodologies and applications of map generalisation’ in Generalisation in practice within national mapping agencies eds BurghardtD MackanessW DucheneC Springer Cham 329 391 Search in Google Scholar

Eckert, M 1908, ‘On the nature of maps and map logic’, Bulletin of the American Geographical Society, vol. 40, no. 6, pp. 344–351. EckertM 1908 ‘On the nature of maps and map logic’ Bulletin of the American Geographical Society 40 6 344 351 Search in Google Scholar

Everett, M & Borgatti, SP 2005, ‘Ego network betweenness’, Social Networks, vol. 27, no. 1, pp. 31–38. EverettM BorgattiSP 2005 ‘Ego network betweenness’ Social Networks 27 1 31 38 Search in Google Scholar

Feng, Y, Thiemann, F & Sester, M 2019, ‘Learning cartographic building generalization with deep convolutional neural networks’, ISPRS International Journal of Geo-Information, vol. 8, no. 6, article number: 258. FengY ThiemannF SesterM 2019 ‘Learning cartographic building generalization with deep convolutional neural networks’ ISPRS International Journal of Geo-Information 8 6 article number: 258. Search in Google Scholar

Fu, C, Zhou, Z, Feng, Y & Weibel, R 2023, ‘Keeping walls straight: Data model and training set size matter for deep learning in building generalization’, Cartography and Geographic Information Science, vol. 51, no. 1, pp. 130–145. FuC ZhouZ FengY WeibelR 2023 ‘Keeping walls straight: Data model and training set size matter for deep learning in building generalization’ Cartography and Geographic Information Science 51 1 130 145 Search in Google Scholar

Gil de Zúñiga, H, Goyanes, M & Durotoye, T 2023, ‘A scholarly definition of artificial intelligence (AI): Advancing AI as a conceptual framework in communication research’, Political Communication, vol. 41, no. 2, pp. 317–334. Gil de ZúñigaH GoyanesM DurotoyeT 2023 ‘A scholarly definition of artificial intelligence (AI): Advancing AI as a conceptual framework in communication research’ Political Communication 41 2 317 334 Search in Google Scholar

Gülgen, F & Gökgöz, T 2011, ‘A block-based selection method for road network generalization’, International Journal of Digital Earth, vol. 4, no. 2, pp. 133–153. GülgenF GökgözT 2011 ‘A block-based selection method for road network generalization’ International Journal of Digital Earth 4 2 133 153 Search in Google Scholar

Guo, X, Liu, J, Wu, F & Qian, H 2023, ‘A method for intelligent road network selection based on graph neural network’, ISPRS International Journal of Geo-Information, vol. 12, no. 8, article number: 336. GuoX LiuJ WuF QianH 2023 ‘A method for intelligent road network selection based on graph neural network’ ISPRS International Journal of Geo-Information 12 8 article number: 336. Search in Google Scholar

Han, Y, Wang, Z, Lu, X & Hu, B 2020, ‘Application of AHP to road selection’, ISPRS International Journal of Geo-Information, vol. 9, no. 2, article number: 86. HanY WangZ LuX HuB 2020 ‘Application of AHP to road selection’ ISPRS International Journal of Geo-Information 9 2 article number: 86. Search in Google Scholar

Harrie, L, Touya, G, Oucheikh, R, Ai, T, Courtial, A & Richter, KF 2024, ‘Machine learning in cartography’, Cartography and Geographic Information Science, vol. 51, no. 1, pp. 1–19. HarrieL TouyaG OucheikhR AiT CourtialA RichterKF 2024 ‘Machine learning in cartography’ Cartography and Geographic Information Science 51 1 1 19 Search in Google Scholar

Jiang, B & Claramunt, C 2004, ‘A structural approach to the model generalization of an urban street network’, GeoInformatica, vol. 8, no. 2, pp. 157–171. JiangB ClaramuntC 2004 ‘A structural approach to the model generalization of an urban street network’ GeoInformatica 8 2 157 171 Search in Google Scholar

Jiang, B & Harrie, L 2004, ‘Selection of Streets from a Network Using Self-Organizing Maps’, Transactions in GIS, vol. 8, no. 3, pp. 335–350. JiangB HarrieL 2004 ‘Selection of Streets from a Network Using Self-Organizing Maps’ Transactions in GIS 8 3 335 350 Search in Google Scholar

Kang, Y, Gao, S & Roth, R 2024, ‘Artificial intelligence studies in cartography: A review and synthesis of methods, applications, and ethics’, Cartography and Geographic Information Science, vol. 51, no. 1, pp. 1–32. KangY GaoS RothR 2024 ‘Artificial intelligence studies in cartography: A review and synthesis of methods, applications, and ethics’ Cartography and Geographic Information Science 51 1 1 32 Search in Google Scholar

Karsznia, I, Wereszczyńska, K & Weibel, R 2022, ‘Make it simple: Effective road selection for small-scale map design using decision-tree-based models’, ISPRS International Journal of Geo-Information, vol. 11, no. 8, article numer: 457. KarszniaI WereszczyńskaK WeibelR 2022 ‘Make it simple: Effective road selection for small-scale map design using decision-tree-based models’ ISPRS International Journal of Geo-Information 11 8 article numer: 457. Search in Google Scholar

Karsznia, I 2023, ‘Machine learning and geospatial technologies’ in The Routledge Handbook of Geospatial Technologies and Society, eds AJ Kent & D Specht, Routledge, London, pp. 467–478. KarszniaI 2023 ‘Machine learning and geospatial technologies’ in The Routledge Handbook of Geospatial Technologies and Society eds KentAJ SpechtD Routledge London 467 478 Search in Google Scholar

Karsznia, I, Adolf, A, Leyk, S & Weibel, R 2024a, ‘Using machine learning and data enrichment in the selection of roads for small-scale maps’, Cartography and Geographic Information Science, vol. 51, no. 1, pp. 60–78. KarszniaI AdolfA LeykS WeibelR 2024a ‘Using machine learning and data enrichment in the selection of roads for small-scale maps’ Cartography and Geographic Information Science 51 1 60 78 Search in Google Scholar

Karsznia, I, Çöltekin, A & Sundstedt, V 2024b, ‘Cartographic generalization of settlement representations: Human vs. machine’, Abstracts of the International Cartographic Association, vol. 8, no. 12. KarszniaI ÇöltekinA SundstedtV 2024b ‘Cartographic generalization of settlement representations: Human vs. machine’ Abstracts of the International Cartographic Association 8 12 Search in Google Scholar

Kim, Y, Yamamoto, D, Fukuyasu, H & Takahashi, N 2019, ‘A road generalization method using layered stroke networks’. in Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Recommendations, Geosocial Networks and Geoadvertising (LocalRec 2019). KimY YamamotoD FukuyasuH TakahashiN 2019 ‘A road generalization method using layered stroke networks’ in Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Recommendations, Geosocial Networks and Geoadvertising (LocalRec 2019) Search in Google Scholar

Kraak, MJ, Roth, RE, Ricker, B, Kagawa, A & Le Sourd, G 2020, Mapping for a sustainable world, United Nations, New York. KraakMJ RothRE RickerB KagawaA Le SourdG 2020 Mapping for a sustainable world United Nations New York Search in Google Scholar

Li, Z & Zhou, Q 2012, ‘Integration of linear and areal hierarchies for continuous multi-scale representation of road networks’, International Journal of Geographical Information Science, vol. 26, no. 5, pp. 855–880. LiZ ZhouQ 2012 ‘Integration of linear and areal hierarchies for continuous multi-scale representation of road networks’ International Journal of Geographical Information Science 26 5 855 880 Search in Google Scholar

Li, C, Wu, W, Wu, P, Yin, J & Guo, P 2020, ‘An elimination method for isolated meshes in a road network considering stroke edge feature’, PLoS ONE, vol. 15, no. 11. LiC WuW WuP YinJ GuoP 2020 ‘An elimination method for isolated meshes in a road network considering stroke edge feature’ PLoS ONE 15 11 Search in Google Scholar

Lisiewicz, M & Karsznia, I 2021, ‘Verifying and exploring settlement selection rules and variables for small-scale maps using decision tree-based models’, Geographical Analysis, vol. 53, no. 3, pp. 560–587 LisiewiczM KarszniaI 2021 ‘Verifying and exploring settlement selection rules and variables for small-scale maps using decision tree-based models’ Geographical Analysis 53 3 560 587 Search in Google Scholar

Liu, X, Zhan, BF & Ai, T 2010, ‘Road selection based on Voronoi diagrams and ‘strokes’ in map generalization’, International Journal of Applied Earth Observation and Geoinformation, vol. 12, no. 1, pp. 194–202. LiuX ZhanBF AiT 2010 ‘Road selection based on Voronoi diagrams and ‘strokes’ in map generalization’ International Journal of Applied Earth Observation and Geoinformation 12 1 194 202 Search in Google Scholar

Liu, Y & Li, W 2019, ‘A new algorithm of stroke generation considering geometric and structural properties of road network’, ISPRS International Journal of Geo-Information, vol. 8, no. 7, article number: 304. LiuY LiW 2019 ‘A new algorithm of stroke generation considering geometric and structural properties of road network’ ISPRS International Journal of Geo-Information 8 7 article number: 304. Search in Google Scholar

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, vol. 11, no. 3, article number: 159. LyuZ SunQ MaJ XuQ LiY ZhangF 2022 ‘Road network generalization method constrained by residential areas’ ISPRS International Journal of Geo-Information 11 3 article number: 159. Search in Google Scholar

Mackaness, WA 1995, ‘Analysis of urban road networks to support cartographic generalization’, Cartography and Geographic Information Science, vol. 22, no. 4, pp. 306–316. MackanessWA 1995 ‘Analysis of urban road networks to support cartographic generalization’ Cartography and Geographic Information Science 22 4 306 316 Search in Google Scholar

Mackaness, WA, & Beard, KM 1993, ‘Use of graph theory to support map generalization’, Cartography and Geographic Information Systems, vol. 20, no. 4, pp. 210–221. MackanessWA BeardKM 1993 ‘Use of graph theory to support map generalization’ Cartography and Geographic Information Systems 20 4 210 221 Search in Google Scholar

Mustière, S 2005, ‘Cartographic generalization of roads in a local and adaptive approach: Knowledge acquisition problem’, International Journal of Geographical Information Science, vol. 19, no. 8–9, pp. 937–955. MustièreS 2005 ‘Cartographic generalization of roads in a local and adaptive approach: Knowledge acquisition problem’ International Journal of Geographical Information Science 19 8–9 937 955 Search in Google Scholar

Pawlak, W 1971, ‘Charakter zniekształceń wybranych elementów treści mapy powstałych w procesie generalizacji’ [The Nature of Distortions of Selected Map Content Elements Resulting from the Generalization Process], Acta Universitatis Wratislaviensis, vol. 113, pp. 112. PawlakW 1971 ‘Charakter zniekształceń wybranych elementów treści mapy powstałych w procesie generalizacji’ [The Nature of Distortions of Selected Map Content Elements Resulting from the Generalization Process] Acta Universitatis Wratislaviensis 113 112 Search in Google Scholar

Pung, J, D’Souza, R, Ghosal, D & Zhang, M 2022, ‘A road network simplification algorithm that preserves topological properties’, Applied Network Science, vol. 7, article number: 79. PungJ D’SouzaR GhosalD ZhangM 2022 ‘A road network simplification algorithm that preserves topological properties’ Applied Network Science 7 article number: 79. Search in Google Scholar

Ruas, A & Morisset, B 1997, ‘Simulation and agent modelling for road selection in generalization’ in Proceedings of the Eighteenth International Cartographic Association Conference, Stockholm, Sweden, pp. 1376–1380. RuasA MorissetB 1997 ‘Simulation and agent modelling for road selection in generalization’ in Proceedings of the Eighteenth International Cartographic Association Conference Stockholm, Sweden 1376 1380 Search in Google Scholar

Ratajski, L 1967, ‘Phenomenes des points de generalization’, ‘International Yearbook of Cartography, vol. 7, pp. 143–151. RatajskiL 1967 ‘Phenomenes des points de generalization’ International Yearbook of Cartography 7 143 151 Search in Google Scholar

Russell, SJ & Norvig, P 1995, Artificial intelligence: A modern approach, Englewood Cliffs, Prentice-Hall, New Jersey. RussellSJ NorvigP 1995 Artificial intelligence: A modern approach Englewood Cliffs, Prentice-Hall New Jersey Search in Google Scholar

Sarjakoski, LT 2007, ‘Conceptual models of generalisation and multiple representation’ in Generalisation of geographic information: Cartographic modelling and applications, eds WA Mackaness, A, Ruas & LT Sarjakoski, Elsevier, Amsterdam, pp.11–35. SarjakoskiLT 2007 ‘Conceptual models of generalisation and multiple representation’ in Generalisation of geographic information: Cartographic modelling and applications eds MackanessWA RuasA SarjakoskiLT Elsevier Amsterdam 11 35 Search in Google Scholar

Sester, M, Feng, Y & Thiemann, F 2018, ‘Building generalization using deep learning’, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, no. 4, pp. 565–572. SesterM FengY ThiemannF 2018 ‘Building generalization using deep learning’ The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42 4 565 572 Search in Google Scholar

Stanislawski, LV, Buttenfield, BP, Bereuter, P, Savino, S & Brewer, CA 2014, ‘Generalization operators’ in Abstracting geographic information in a data rich world: Methodologies and applications of map generalisation, eds D Burghardt, C Duchêne & W Mackaness, Springer, Cham, pp. 157–196. StanislawskiLV ButtenfieldBP BereuterP SavinoS BrewerCA 2014 ‘Generalization operators’ in Abstracting geographic information in a data rich world: Methodologies and applications of map generalisation eds BurghardtD DuchêneC MackanessW Springer Cham 157 196 Search in Google Scholar

Tang, J, Deng, M, Peng, J, Liu, H, Yang, X & Chen, X 2024, ‘Automatic road network selection method considering functional semantic features of roads with graph convolutional networks’, International Journal of Geographical Information Science, vol. 38, no. 11, pp. 2403–2432. TangJ DengM PengJ LiuH YangX ChenX 2024 ‘Automatic road network selection method considering functional semantic features of roads with graph convolutional networks’ International Journal of Geographical Information Science 38 11 2403 2432 Search in Google Scholar

Thomson, RC & Brooks, R 2000, ‘Efficient generalization and abstraction of network data using perceptual grouping’ in Proceedings of the 5th International Conference on GeoComputation. Greenwich, UK. ThomsonRC BrooksR 2000 ‘Efficient generalization and abstraction of network data using perceptual grouping’ in Proceedings of the 5th International Conference on GeoComputation Greenwich, UK Search in Google Scholar

Touya, G 2010, ‘A road network selection process based on data enrichment and structure detection’, Transactions in GIS, vol. 14, no. 5, pp. 595–614. TouyaG 2010 ‘A road network selection process based on data enrichment and structure detection’ Transactions in GIS 14 5 595 614 Search in Google Scholar

Weibel, R 1995, ‘Three essential building blocks for automated generalization’ in GIS and generalization: Methodology and practice, eds JC Müller, JP Lagrange & R Weibel, R, Taylor & Francis, London, pp. 3–17. WeibelR 1995 ‘Three essential building blocks for automated generalization’ in GIS and generalization: Methodology and practice eds MüllerJC LagrangeJP WeibelR TaylorR Francis London 3 17 Search in Google Scholar

Weibel, R, Keller, S & Reichenbacher, T 1995, ‘Overcoming the knowledge acquisition bottleneck in map generalization: The role of interactive systems and computational intelligence’ in Proceedings of COSIT ‘95, eds A Frank & W Kuhn, Springer, Berlin, pp. 139–156. WeibelR KellerS ReichenbacherT 1995 ‘Overcoming the knowledge acquisition bottleneck in map generalization: The role of interactive systems and computational intelligence’ in Proceedings of COSIT ‘95 eds FrankA KuhnW Springer Berlin 139 156 Search in Google Scholar

Weiss, R & Weibel, R 2014, ‘Road network selection for small-scale maps using an improved centrality-based algorithm’, Journal of Spatial Information Science, vol. 9, pp. 71–99. WeissR WeibelR 2014 ‘Road network selection for small-scale maps using an improved centrality-based algorithm’ Journal of Spatial Information Science 9 71 99 Search in Google Scholar

Wu, P, Yin, Y, Wu, C, Bai, X, Zhang, C & Dai, Z 2022, ‘An automatic generalization method for a dense road network area considering spatial structural features as constraints’, ISPRS International Journal of Geo-Information, vol. 11, no. 12, article number: 599. WuP YinY WuC BaiX ZhangC DaiZ 2022 ‘An automatic generalization method for a dense road network area considering spatial structural features as constraints’ ISPRS International Journal of Geo-Information 11 12 article number: 599. Search in Google Scholar

Xiao, T, Al, T, Burghardt, D, Liu, P, Yang, M, Gao, A, Kong, B & Yu, H 2024, ‘A road generalization method using graph convolutional network based on mesh-line structure unit’, Geocarto International, vol. 39, no. 1. XiaoT AlT BurghardtD LiuP YangM GaoA KongB YuH 2024 ‘A road generalization method using graph convolutional network based on mesh-line structure unit’ Geocarto International 39 1 Search in Google Scholar

Xu, Y, Xie, Z, Wu, L & Chen, Z 2019, ‘Multilane roads extracted from the OpenStreetMap urban road network using random forests’, Transactions in GIS, vol. 23, no. 2, pp. 224–240. XuY XieZ WuL ChenZ 2019 ‘Multilane roads extracted from the OpenStreetMap urban road network using random forests’ Transactions in GIS 23 2 224 240 Search in Google Scholar

Yan, C, Liu, X, Boota, MW & Pan, Z 2022, ‘A comparison of machine learning methods applied to the automated selection of river networks’, The Cartographic Journal, vol. 59, no. 2, pp.187–202. YanC LiuX BootaMW PanZ 2022 ‘A comparison of machine learning methods applied to the automated selection of river networks’ The Cartographic Journal 59 2 187 202 Search in Google Scholar

Yan, X & Yang, M 2023, ‘A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints’, Cartography and Geographic Information Science, vol. 51, no. 1, pp. 1–18. YanX YangM 2023 ‘A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints’ Cartography and Geographic Information Science 51 1 1 18 Search in Google Scholar

Yang, B, Luan, X & Li, Q 2011, ‘Generating hierarchical strokes from urban street networks based on spatial pattern recognition’, International Journal of Geographical Information Science, vol. 25, no. 11, pp. 2025–2050. YangB LuanX LiQ 2011 ‘Generating hierarchical strokes from urban street networks based on spatial pattern recognition’ International Journal of Geographical Information Science 25 11 2025 2050 Search in Google Scholar

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, vol. 36, no. 2, pp. 280–309. YangM YuanT YanX AiT JiangC 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 Search in Google Scholar

Yu, W & Chen, Y 2022, ‘Data-driven polyline simplification using a stacked autoencoder-based deep neural network’, Transactions in GIS, vol. 26, no. 4, pp. 2302–2325. YuW ChenY 2022 ‘Data-driven polyline simplification using a stacked autoencoder-based deep neural network’ Transactions in GIS 26 4 2302 2325 Search in Google Scholar

Zhang, H, & Li, Z 2011, ‘Weighted ego network for forming hierarchical structure of road networks’, International Journal of Geographical Information Science, vol. 25, no. 2, pp. 255–272. ZhangH, LiZ 2011 ‘Weighted ego network for forming hierarchical structure of road networks’ International Journal of Geographical Information Science 25 2 255 272 Search in Google Scholar

Zhang, J, Wang, Y & Zhao, W 2017, ‘An improved hybrid method for enhanced road feature selection in map generalization’, ISPRS International Journal of Geo-Information, vol. 6, no. 7, article number: 196. ZhangJ WangY ZhaoW 2017 ‘An improved hybrid method for enhanced road feature selection in map generalization’ ISPRS International Journal of Geo-Information 6 7 article number: 196. Search in Google Scholar

Zheng, J, Gao, Z, Ma, J, Shen, J & Zhang, K 2021, ‘Deep graph convolutional networks for accurate automatic road network selection’, ISPRS International Journal of Geo-Information, vol. 10, no. 11, article number: 768. ZhengJ GaoZ MaJ ShenJ ZhangK 2021 ‘Deep graph convolutional networks for accurate automatic road network selection’ ISPRS International Journal of Geo-Information 10 11 article number: 768. Search in Google Scholar

Zheng, H, Zhang, J, Li, H, Wang, G, Guo J, Wang, J 2024, ‘Road Network Intelligent Selection Method based on Heterogeneous Graph Attention Neural Network’, ISPRS International Journal of Geo-Information. vol. 13, no. 9. ZhengH ZhangJ LiH WangG GuoJ WangJ 2024 ‘Road Network Intelligent Selection Method based on Heterogeneous Graph Attention Neural Network’ ISPRS International Journal of Geo-Information 13 9 Search in Google Scholar

Zhou, Q & Li, Z 2014, ‘Use of Artificial neural networks for selective omission in updating road networks’, The Cartographic Journal, vol. 51, no. 1, pp. 38–51. ZhouQ LiZ 2014 ‘Use of Artificial neural networks for selective omission in updating road networks’ The Cartographic Journal 51 1 38 51 Search in Google Scholar

Zhou, Q & Li, Z 2017, ‘A comparative study of various supervised learning approaches to selective omission in a road network’, The Cartographic Journal, vol. 54, no. 3, pp. 254–264. ZhouQ LiZ 2017 ‘A comparative study of various supervised learning approaches to selective omission in a road network’ The Cartographic Journal 54 3 254 264 Search in Google Scholar

Zhou, Z, Fu, C & Weibel, R 2022, ‘Building simplification of vector maps using graph convolutional neural networks’, Abstracts of the ICA, vol. 5, no. 1. ZhouZ FuC WeibelR 2022 ‘Building simplification of vector maps using graph convolutional neural networks’ Abstracts of the ICA 5 1 Search in Google Scholar