{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T16:50:21Z","timestamp":1771260621460,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871378"],"award-info":[{"award-number":["41871378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171438"],"award-info":[{"award-number":["42171438"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Multi-scale electronic map tiles are important basic geographic information data, and an approach based on deep learning is being used to generate multi-scale map tiles. Although generative adversarial networks (GANs) have demonstrated great potential in single-scale electronic map tile generation, further research concerning multi-scale electronic map tile generation is needed to meet cartographic requirements. We designed a multi-scale electronic map tile generative adversarial network (MsM-GAN), which consisted of several GANs and could generate map tiles at different map scales sequentially. Road network data and building footprint data from OSM (Open Street Map) were used as auxiliary information to provide the MsM-GAN with cartographic knowledge about spatial shapes and spatial relationships when generating electronic map tiles from remote sensing images. The map objects which should be deleted or retained at the next map scale according to cartographic standards are encoded as auxiliary information in the MsM-GAN when generating electronic map tiles at smaller map scales. In addition, in order to ensure the consistency of the features learned by several GANs, the density maps constructed from specific map objects are used as global conditions in the MsM-GAN. A multi-scale map tile dataset was collected from MapWorld, and experiments on this dataset were conducted using the MsM-GAN. The results showed that compared to other image-to-image translation models (Pix2Pix and CycleGAN), the MsM-GAN shows average increases of 10.47% in PSNR and 9.92% in SSIM and has the minimum MSE values at all four map scales. The MsM-GAN also performs better in visual evaluation. In addition, several comparative experiments were completed to verify the effect of the proposed improvements.<\/jats:p>","DOI":"10.3390\/ijgi13110398","type":"journal-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T06:41:22Z","timestamp":1730961682000},"page":"398","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Improved Generative Adversarial Network for Generating Multi-Scale Electronic Map Tiles Considering Cartographic Requirements"],"prefix":"10.3390","volume":"13","author":[{"given":"Wei","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5863-1946","authenticated-orcid":false,"given":"Qingsheng","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5306-1163","authenticated-orcid":false,"given":"Nai","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Tong","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5513-3788","authenticated-orcid":false,"given":"Chuanbang","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"ref_1","first-page":"1226","article-title":"Cartography in the age of spatio-temporal big data","volume":"46","author":"Wang","year":"2017","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_2","first-page":"1170","article-title":"Some thoughts on deep learning enabling cartography","volume":"50","author":"Ai","year":"2021","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1080\/15230406.2023.2295943","article-title":"Artificial intelligence studies in cartography: A review and synthesis of methods, applications, and ethics","volume":"51","author":"Kang","year":"2024","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bastani, F., and Madden, S. (2021, January 11\u201317). Beyond road extraction: A dataset for map update using aerial images. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, Canada.","DOI":"10.1109\/ICCV48922.2021.01169"},{"key":"ref_5","first-page":"1","article-title":"Multilevel mapping from remote sensing images: A case study of urban buildings","volume":"60","author":"Shen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1080\/15230406.2023.2267419","article-title":"DeepMapScaler: A workflow of deep neural networks for the generation of generalised maps","volume":"51","author":"Courtial","year":"2024","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1080\/15230406.2023.2218106","article-title":"A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints","volume":"51","author":"Yan","year":"2024","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Stanislawski, L.V., Buttenfield, B.P., Bereuter, P., Savino, S., Brewer, C.A., and Stanislawski, L.V. (2014). Generalisation Operators. Abstracting Geographic Information in a Data Rich World: Methodologies and Applications of Map Generalization, Springer International Publishing.","DOI":"10.1007\/978-3-319-00203-3_6"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, Z., Zhao, X., and Shao, L. (2020). MapGAN: An intelligent generation model for network tile maps. Sensors, 20.","DOI":"10.3390\/s20113119"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4388","DOI":"10.1109\/TGRS.2020.3021819","article-title":"SMAPGAN: Generative adversarial network-based semi-supervised styled map tile generation method","volume":"59","author":"Chen","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","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_12","first-page":"1","article-title":"Generating multiscale maps from satellite images via series generative adversarial networks","volume":"19","author":"Chen","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1559\/152304007781697866","article-title":"Relations among map objects in cartographic generalization","volume":"34","author":"Steiniger","year":"2007","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1080\/13658816.2022.2041643","article-title":"GANmapper: Geographical data translation","volume":"36","author":"Wu","year":"2022","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"176704","DOI":"10.1109\/ACCESS.2020.3025008","article-title":"An enhanced GAN model for automatic satellite-to-map image conversion","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z.H., Tay, F.E., Feng, J., and Yan, S. (2021, January 11\u201317). Tokens-to-Token Vit: Training Vision Transformers from Scratch on ImageNet. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, Canada.","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10850","DOI":"10.1109\/TPAMI.2023.3261988","article-title":"Diffusion models in vision: A survey","volume":"45","author":"Croitoru","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104646","DOI":"10.1016\/j.autcon.2022.104646","article-title":"Pavement crack detection based on transformer network","volume":"145","author":"Guo","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"10346","DOI":"10.1109\/TPAMI.2023.3238179","article-title":"Restoring vision in adverse weather conditions with patch-based denoising diffusion models","volume":"45","author":"Legenstein","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative adversarial networks: An overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.neucom.2020.12.114","article-title":"The theoretical research of generative adversarial networks: An overview","volume":"435","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_22","unstructured":"Radford, A. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_24","unstructured":"Zu, J.-Y., Park, T., Isola, P., and Erfos, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7178","DOI":"10.1109\/TGRS.2020.2980417","article-title":"ColorMapGAN: Unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks","volume":"58","author":"Tasar","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1109\/ACCESS.2020.3015656","article-title":"A realistic image generation of face from text description using the fully trained generative adversarial networks","volume":"9","author":"Khan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"63514","DOI":"10.1109\/ACCESS.2020.2982224","article-title":"A state-of-the-art review on image synthesis with generative adversarial networks","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1080\/15230406.2023.2264757","article-title":"Keeping walls straight: Data model and training set size matter for deep learning in building generalization","volume":"51","author":"Fu","year":"2024","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9355","DOI":"10.1080\/10106049.2021.2017017","article-title":"Mapping land cover using a developed U-Net model with weighted cross entropy","volume":"37","author":"Sun","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shan, B., and Fang, Y. (2020). A cross entropy based deep neural network model for road extraction from satellite images. Entropy, 22.","DOI":"10.3390\/e22050535"},{"key":"ref_31","unstructured":"Ganguli, S., Garzon, P., and Glaser, N. (2019). GeoGAN: A conditional GAN with reconstruction and style loss to generate standard layer of maps from satellite images. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhou, X., and Han, C. (2023). SAM-GAN: Supervised Learning-Based Aerial Image-to-Map Translation via Generative Adversarial Networks. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12040159"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.3390\/ijgi4031246","article-title":"A progressive buffering method for road map update using OpenStreetMap data","volume":"4","author":"Liu","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.compenvurbsys.2018.08.004","article-title":"Predicting residential building age from map data","volume":"73","author":"Rosser","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1080\/17538947.2020.1715495","article-title":"A progressive method for the collapse of river representation considering geographical characteristics","volume":"13","author":"Shen","year":"2020","journal-title":"Int. J. Digit. Earth"},{"key":"ref_38","first-page":"5","article-title":"Generative adversarial networks to generalise urban areas in topographic maps. 2021 The International Archives of the Photogrammetry","volume":"XLIII-B4","author":"Courtial","year":"2021","journal-title":"Remote Sens. Spat. Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"112589","DOI":"10.1016\/j.rse.2021.112589","article-title":"Deep building footprint update network: A semi-supervised method for updating existing building footprint from bi-temporal remote sensing images","volume":"264","author":"Guo","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, W., Fang, F., Zhou, L., Sun, C., Zheng, Y., and Chen, Z. (2021). CscGAN: Conditional scale-consistent generation network for multi-level remote sensing image to map translation. Remote Sens., 13.","DOI":"10.3390\/rs13101936"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1049\/el:20080522","article-title":"Scope of validity of PSNR in image\/video quality assessment","volume":"44","author":"Ghanbari","year":"2008","journal-title":"Electron. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hore, A., and Ziou, D. (2010, January 23\u201326). Image quality metrics: PSNR vs. SSIM. Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1080\/17538947.2010.510305","article-title":"Generalisation, symbol specification and map evaluation: Feedback from research done at COGIT laboratory, IGN France","volume":"4","author":"Christophe","year":"2011","journal-title":"Int. J. Digit. Earth"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Mackaness, W.A., and Ruas, A. (2007). Evaluation in the Map Generalisation Process. Generalisation of Geographic Information, Elsevier Science BV.","DOI":"10.1016\/B978-008045374-3\/50007-7"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/11\/398\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:27:44Z","timestamp":1760113664000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/11\/398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,7]]},"references-count":45,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["ijgi13110398"],"URL":"https:\/\/doi.org\/10.3390\/ijgi13110398","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,7]]}}}