{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T23:36:28Z","timestamp":1768260988824,"version":"3.49.0"},"reference-count":73,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,16]],"date-time":"2023-04-16T00:00:00Z","timestamp":1681603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AEI","award":["PID2020-116448GB-I00"],"award-info":[{"award-number":["PID2020-116448GB-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Most existing road extraction approaches apply learning models based on semantic segmentation networks and consider reduced study areas, featuring favorable scenarios. In this work, an end-to-end processing strategy to extract the road surface areas from aerial orthoimages at the scale of the national territory is proposed. The road mapping solution is based on the consecutive execution of deep learning (DL) models trained for \u2460 road recognition, \u2461 semantic segmentation of road surface areas, and \u2462 post-processing of the initial predictions with conditional generative learning, within the same processing environment. The workflow also involves steps such as checking if the aerial image is found within the country\u2019s borders, performing the three mentioned DL operations, applying a p=0.5 decision limit to the class predictions, or considering only the central 75% of the image to reduce prediction errors near the image boundaries. Applying the proposed road mapping solution translates to operations aimed at checking if the latest existing cartographic support (aerial orthophotos divided into tiles of 256 \u00d7 256 pixels) contains the continuous geospatial element, to obtain a linear approximation of its geometry using supervised learning, and to improve the initial semantic segmentation results with post-processing based on image-to-image translation. The proposed approach was implemented and tested on the openly available benchmarking SROADEX dataset (containing more than 527,000 tiles covering approximately 8650 km2 of the Spanish territory) and delivered a maximum increase in performance metrics of 10.6% on unseen, testing data. The predictions on new areas displayed clearly higher quality when compared to existing state-of-the-art implementations trained for the same task.<\/jats:p>","DOI":"10.3390\/rs15082099","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T02:02:59Z","timestamp":1681696979000},"page":"2099","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["State-Level Mapping of the Road Transport Network from Aerial Orthophotography: An End-to-End Road Extraction Solution Based on Deep Learning Models Trained for Recognition, Semantic Segmentation and Post-Processing with Conditional Generative Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7713-7238","authenticated-orcid":false,"given":"Calimanut-Ionut","family":"Cira","sequence":"first","affiliation":[{"name":"Departamento de Ingenier\u00eda Topogr\u00e1fica y Cartograf\u00eda, E.T.S.I. en Topograf\u00eda, Geodesia y Cartograf\u00eda, Universidad Polit\u00e9cnica de Madrid, C\/Mercator 2, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2307-8639","authenticated-orcid":false,"given":"Miguel-\u00c1ngel","family":"Manso-Callejo","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Topogr\u00e1fica y Cartograf\u00eda, E.T.S.I. en Topograf\u00eda, Geodesia y Cartograf\u00eda, Universidad Polit\u00e9cnica de Madrid, C\/Mercator 2, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1183-9579","authenticated-orcid":false,"given":"Ram\u00f3n","family":"Alcarria","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Topogr\u00e1fica y Cartograf\u00eda, E.T.S.I. en Topograf\u00eda, Geodesia y Cartograf\u00eda, Universidad Polit\u00e9cnica de Madrid, C\/Mercator 2, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7815-5924","authenticated-orcid":false,"given":"Borja","family":"Bordel S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Departamento de Sistemas Inform\u00e1ticos, E.T.S.I. de Sistemas Inform\u00e1ticos, Universidad Polit\u00e9cnica de Madrid, C\/Alan Turing, s\/n, 28031 Madrid, Spain"}]},{"given":"Javier","family":"Gonz\u00e1lez Matesanz","sequence":"additional","affiliation":[{"name":"Subdirecci\u00f3n General de Geodesia y Cartograf\u00eda, Direcci\u00f3n General del Instituto Geogr\u00e1fico Nacional, C\/Gral. Ib\u00e1\u00f1ez de Ibero 3, 28003 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,16]]},"reference":[{"key":"ref_1","unstructured":"(2023, March 22). Instituto Geogr\u00e1fico Nacional (Spain) Especificaciones de Producto de Redes e Infraestructuras del Transporte del Instituto Geogr\u00e1fico Nacional. Available online: http:\/\/www.ign.es\/resources\/IGR\/Transporte\/20160316_Espec_RT_V0.5.pdf."},{"key":"ref_2","unstructured":"Bengio, Y., and LeCun, Y. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA. Conference Track Proceedings."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. 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