{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:36:39Z","timestamp":1760146599932,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42271451"],"award-info":[{"award-number":["42271451"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>As the complexity of GIS data continues to increase, there is a growing demand for automated map generalization. As end-to-end generative models, GAN models offer new solutions for automated map generalization. This study explores the impact of different map symbolization configurations on generative models, specifically using CycleGAN for line feature generalization. The quality of the generated results was assessed by constructing various symbolization datasets (line width, type, and color) and evaluating CycleGAN\u2019s performance using metrics such as the MSE, SSIM, and PSNR. The results indicate that moderate line widths (0.5\u20131) yield better detail preservation, and different line types (framed lines and dashed lines) can highlight feature boundaries and enhance visual perception. By contrast, high-contrast color schemes enhance feature differentiation but increase pixel-level errors. This study concludes that generative models can maintain the geometric structure and spatial distribution of line features, but it is crucial to choose more suitable line features for different scenarios to meet detail requirements, ensuring high-quality outputs under diverse configurations.<\/jats:p>","DOI":"10.3390\/ijgi13120418","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T06:11:54Z","timestamp":1732169514000},"page":"418","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Assessment of the Map-Style Influence on Generalization with CycleGAN: Taking Line Features as an Example"],"prefix":"10.3390","volume":"13","author":[{"given":"Heng","family":"Yu","sequence":"first","affiliation":[{"name":"Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China"},{"name":"State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoxuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China"},{"name":"State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3228-8374","authenticated-orcid":false,"given":"Ling","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China"},{"name":"State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, Z. (2007). Algorithmic Foundation of Multi-Scale Spatial Representation, CRC.","DOI":"10.1201\/9781420008432"},{"key":"ref_2","first-page":"5","article-title":"Cartographic Generalization","volume":"21","author":"Sester","year":"2020","journal-title":"J. Spat. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15230406.2023.2295948","article-title":"Machine Learning in Cartography","volume":"51","author":"Harrie","year":"2024","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1080\/23729333.2019.1613071","article-title":"Is Deep Learning the New Agent for Map Generalization?","volume":"5","author":"Touya","year":"2019","journal-title":"Int. J. Cartogr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"157","DOI":"10.5194\/isprs-annals-IV-2-W5-157-2019","article-title":"Vector map generation from aerial imagery using deep learning","volume":"4","author":"Sahu","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1080\/15230406.2023.2283075","article-title":"Using Machine Learning and Data Enrichment in the Selection of Roads for Small-Scale Maps","volume":"51","author":"Karsznia","year":"2024","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1111\/gean.12246","article-title":"Verifying and Exploring Settlement Selection Rules and Variables for Small-Scale Maps Using Decision Tree-Based Models","volume":"53","author":"Lisiewicz","year":"2021","journal-title":"Geogr. Anal."},{"key":"ref_8","first-page":"1910","article-title":"A Habitation Selection Method by Using Case-Based Reasoning","volume":"46","author":"Xie","year":"2017","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_9","first-page":"740","article-title":"Auto-Selection of Areal Habitation Based on Analytic Hierarchy Process","volume":"45","author":"Hu","year":"2016","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1080\/15481603.2013.823748","article-title":"Building Simplification Using Backpropagation Neural Networks: A Combination of Cartographers\u2019 Expertise and Raster-Based Local Perception","volume":"50","author":"Cheng","year":"2013","journal-title":"GIScience Remote Sens."},{"key":"ref_11","first-page":"565","article-title":"Building Generalization Using Deep Learning","volume":"XLII\u20134","author":"Sester","year":"2018","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15","DOI":"10.5194\/isprs-archives-XLIII-B4-2021-15-2021","article-title":"Generative Adversarial Networks to Generalise Urban Areas in Topographic Maps","volume":"43","author":"Courtial","year":"2021","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4158","DOI":"10.1080\/10106049.2021.1878288","article-title":"Segmentation and Sampling Method for Complex Polyline Generalization Based on a Generative Adversarial Network","volume":"37","author":"Du","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1080\/13658816.2022.2123488","article-title":"Deriving Map Images of Generalised Mountain Roads with Generative Adversarial Networks","volume":"37","author":"Courtial","year":"2023","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"180","DOI":"10.5194\/ica-abs-7-180-2024","article-title":"Deep Learning for Map Generalization: Experimenting with Coastline Data at Different Map Scales","volume":"7","author":"Viore","year":"2024","journal-title":"Abstr. Int. Cartogr. Assoc."},{"key":"ref_16","first-page":"21","article-title":"Deep Learning for Enrichment of Vector Spatial Databases","volume":"6","author":"Touya","year":"2020","journal-title":"ACM Trans. Spat. Algorithms Syst. TSAS"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"32","DOI":"10.5194\/agile-giss-3-32-2022","article-title":"Representing Vector Geographic Information As a Tensor for Deep Learning Based Map Generalisation","volume":"3","author":"Courtial","year":"2022","journal-title":"AGILE GIScience Ser."},{"key":"ref_18","first-page":"102696","article-title":"A Recognition Method for Drainage Patterns Using a Graph Convolutional Network","volume":"107","author":"Yu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2302","DOI":"10.1111\/tgis.12965","article-title":"Data-driven Polyline Simplification Using a Stacked Autoencoder-based Deep Neural Network","volume":"26","author":"Yu","year":"2022","journal-title":"Trans. GIS"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1080\/15230406.2023.2187886","article-title":"A Point Selection Method in Map Generalization Using Graph Convolutional Network Model","volume":"51","author":"Xiao","year":"2024","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4388","DOI":"10.1109\/TGRS.2020.3021819","article-title":"SMAPGAN: Generative Adversarial Network-Based Semisupervised Styled Map Tile Generation Method","volume":"59","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1080\/23729333.2019.1615729","article-title":"Transferring Multiscale Map Styles Using Generative Adversarial Networks","volume":"5","author":"Kang","year":"2019","journal-title":"Int. J. Cartogr."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision 2017, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_24","first-page":"132","article-title":"Coarse-to-Fine Semantic Road Segmentation Using Super-Pixel Data Model and Semi-Supervised Modified CycleGAN","volume":"10","author":"Zohourian","year":"2022","journal-title":"J. Image Graph."},{"key":"ref_25","unstructured":"Kang, Y., Rao, J., Wang, W., Peng, B., Gao, S., and Zhang, F. (2020, January 20\u201322). Towards Cartographic Knowledge Encoding with Deep Learning: A Case Study of Building Generalization. Proceedings of the AutoCarto, Online."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Weibel, R., and Burgardt, D. (2008). On-the-Fly Generalization. Encyclopedia of GIS, Springer.","DOI":"10.1007\/978-0-387-35973-1_450"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s13218-022-00794-2","article-title":"Pragmatic GeoAI: Geographic Information as Externalized Practice","volume":"37","author":"Scheider","year":"2023","journal-title":"KI Kunstl. Intell."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/12\/418\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:36:41Z","timestamp":1760114201000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/12\/418"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,21]]},"references-count":27,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["ijgi13120418"],"URL":"https:\/\/doi.org\/10.3390\/ijgi13120418","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2024,11,21]]}}}