{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T03:27:08Z","timestamp":1775705228693,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003329","name":"Ministry of Economy, Industry and Competitiveness","doi-asserted-by":"publisher","award":["PID2020-117142GB-I00"],"award-info":[{"award-number":["PID2020-117142GB-I00"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>There is a growing interest in the development of automated data processing workflows that provide reliable, high spatial resolution land cover maps. However, high-resolution remote sensing images are not always affordable. Taking into account the free availability of Sentinel-2 satellite data, in this work we propose a deep learning model to generate high-resolution segmentation maps from low-resolution inputs in a multi-task approach. Our proposal is a dual-network model with two branches: the Single Image Super-Resolution branch, that reconstructs a high-resolution version of the input image, and the Semantic Segmentation Super-Resolution branch, that predicts a high-resolution segmentation map with a scaling factor of 2. We performed several experiments to find the best architecture, training and testing on a subset of the S2GLC 2017 dataset. We based our model on the DeepLabV3+ architecture, enhancing the model and achieving an improvement of 5% on IoU and almost 10% on the recall score. Furthermore, our qualitative results demonstrate the effectiveness and usefulness of the proposed approach.<\/jats:p>","DOI":"10.3390\/rs13224547","type":"journal-article","created":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T20:51:53Z","timestamp":1636923113000},"page":"4547","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4133-6283","authenticated-orcid":false,"given":"Sa\u00fcc","family":"Abadal","sequence":"first","affiliation":[{"name":"Signal Theory and Communications Department, Universitat Polit\u00e8cnica de Catalunya (UPC), 08034 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4048-8330","authenticated-orcid":false,"given":"Luis","family":"Salgueiro","sequence":"additional","affiliation":[{"name":"Signal Theory and Communications Department, Universitat Polit\u00e8cnica de Catalunya (UPC), 08034 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9646-1017","authenticated-orcid":false,"given":"Javier","family":"Marcello","sequence":"additional","affiliation":[{"name":"Instituto de Oceanograf\u00eda y Cambio Global (IOCAG), Unidad Asociada ULPGC-CSIC, 35017 Las Palmas de Gran Canaria, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6924-9961","authenticated-orcid":false,"given":"Ver\u00f3nica","family":"Vilaplana","sequence":"additional","affiliation":[{"name":"Signal Theory and Communications Department, Universitat Polit\u00e8cnica de Catalunya (UPC), 08034 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Venter, Z.S., and Sydenham, M.A.K. 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