{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:50:12Z","timestamp":1787028612029,"version":"3.56.0"},"reference-count":23,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T00:00:00Z","timestamp":1626825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["679097"],"award-info":[{"award-number":["679097"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Scanned historical maps are available from different sources in various scales and contents. Automatic geographical feature extraction from these historical maps is an essential task to derive valuable spatial information on the characteristics and distribution of transportation infrastructures and settlements and to conduct quantitative and geometrical analysis. In this research, we used the Deutsche Heereskarte 1:200,000 T\u00fcrkei (DHK 200 Turkey) maps as the base geoinformation source to construct the past transportation networks using the deep learning approach. Five different road types were digitized and labeled to be used as inputs for the proposed deep learning-based segmentation approach. We adapted U-Net++ and ResneXt50_32\u00d74d architectures to produce multi-class segmentation masks and perform feature extraction to determine various road types accurately. We achieved remarkable results, with 98.73% overall accuracy, 41.99% intersection of union, and 46.61% F1 score values. The proposed method can be implemented in DHK maps of different countries to automatically extract different road types and used for transfer learning of different historical maps.<\/jats:p>","DOI":"10.3390\/ijgi10080492","type":"journal-article","created":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T11:53:23Z","timestamp":1626868403000},"page":"492","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Automatic Road Extraction from Historical Maps Using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II Map"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7014-1907","authenticated-orcid":false,"given":"Burak","family":"Ekim","sequence":"first","affiliation":[{"name":"Satellite Communication and Remote Sensing Program, Institute of Informatics, Istanbul Technical University, Istanbul 34469, Turkey"},{"name":"Department of History, College of Social Sciences and Humanities, Ko\u00e7 University, Rumelifeneri Yolu, Istanbul 34450, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-494X","authenticated-orcid":false,"given":"Elif","family":"Sertel","sequence":"additional","affiliation":[{"name":"Geomatics Engineering Department, Istanbul Technical University, Istanbul 34469, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3206-0190","authenticated-orcid":false,"given":"M. Erdem","family":"Kabaday\u0131","sequence":"additional","affiliation":[{"name":"Department of History, College of Social Sciences and Humanities, Ko\u00e7 University, Rumelifeneri Yolu, Istanbul 34450, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10032-011-0177-1","article-title":"A General Approach for Extracting Road Vector Data from Raster Maps","volume":"16","author":"Chiang","year":"2013","journal-title":"Int. J. Doc. Anal. Recognit."},{"key":"ref_2","unstructured":"Andrade, H.J.A., and Fernandes, B.J.T. (2020). Synthesis of Satellite-Like Urban Images from Historical Maps Using Conditional GAN. IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chiang, Y.-Y., Duan, W., Leyk, S., Uhl, J.H., and Knoblock, C.A. (2020). Using Historical Maps in Scientific Studies, Springer International Publishing. Springer Briefs in Geography.","DOI":"10.1007\/978-3-319-66908-3"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.cag.2011.01.002","article-title":"Aligning Archive Maps and Extracting Footprints for Analysis of Historic Urban Environments","volume":"35","author":"Laycock","year":"2011","journal-title":"Comput. Graph."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2557423","article-title":"A Survey of Digital Map Processing Techniques","volume":"47","author":"Chiang","year":"2014","journal-title":"ACM Comput. Surv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6978","DOI":"10.1109\/ACCESS.2019.2963213","article-title":"Automated Extraction of Human Settlement Patterns from Historical Topographic Map Series Using Weakly Supervised Convolutional Neural Networks","volume":"8","author":"Uhl","year":"2020","journal-title":"IEEE Access"},{"key":"ref_7","unstructured":"Scharfe, W. (2003, January 10\u201316). German Army Map of Spain 1:50.000: 1940\u20131944. Proceedings of the 21st International Cartographic Conference, Durban, South Africa."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"63","DOI":"10.30897\/ijegeo.303545","article-title":"High Resolution Mapping of Urban Areas Using SPOT-5 Images and Ancillary Data","volume":"2","author":"Sertel","year":"2015","journal-title":"Int. J. Environ. Geoinform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"62847","DOI":"10.1109\/ACCESS.2021.3074897","article-title":"Automatic Detection of Road Types from the Third Military Mapping Survey of Austria-Hungary Historical Map Series with Deep Convolutional Neural Networks","volume":"9","author":"Can","year":"2021","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Alganci, U., Soydas, M., and Sertel, E. (2020). Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images. Remote Sens., 12.","DOI":"10.3390\/rs12030458"},{"key":"ref_11","unstructured":"Zhu, X.X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., and Fraundorfer, F. (2017). Deep Learning in Remote Sensing: A Review. IEEE Geosci. Remote Sens. Mag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","article-title":"Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"114417","DOI":"10.1016\/j.eswa.2020.114417","article-title":"A Review of Deep Learning Methods for Semantic Segmentation of Remote Sensing Imagery","volume":"169","author":"Yuan","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1080\/13658816.2019.1696968","article-title":"Automatic Extraction of Road Intersection Points from USGS Historical Map Series Using Deep Convolutional Neural Networks","volume":"34","author":"Saeedimoghaddam","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ustaoglu, E., Kabaday\u0131, M.E., and Gerrits, P.J. (2021). The Estimation of Non-Irrigated Crop Area and Production Using the Regression Analysis Approach: A Case Study of Bursa Region (Turkey) in the Mid-Nineteenth Century. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0251091"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Long","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","article-title":"Unet++: A Nested u-Net Architecture for Medical Image Segmentation","volume":"11045","author":"Zhou","year":"2018","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., and Cardoso, M.J. (2017). Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3137","DOI":"10.1080\/01431160701442120","article-title":"Harshness in image classification accuracy assessment","volume":"29","author":"Foody","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1071\/WF01031","article-title":"Accuracy assessment and validation of remotely sensed and other spatial information","volume":"10","author":"Congalton","year":"2001","journal-title":"Int. J. Wildland Fire"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/8\/492\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:32:39Z","timestamp":1760164359000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/8\/492"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,21]]},"references-count":23,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["ijgi10080492"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10080492","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,21]]}}}