{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T03:51:02Z","timestamp":1773114662851,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,4,14]],"date-time":"2019-04-14T00:00:00Z","timestamp":1555200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Motivated by the increasing availability of open and free Earth observation data through the Copernicus Sentinel missions, this study investigates the capacity of advanced computational models to automatically generate thematic layers, which in turn contribute to and facilitate the creation of land cover products. In concrete terms, we assess the practical and computational aspects of multi-class Sentinel-2 image segmentation based on a convolutional neural network and random forest approaches. The annotated learning set derives from data that is made available as result of the implementation of European Union\u2019s INSPIRE Directive. Since this network of data sets remains incomplete in regard to some geographic areas, another objective of this work was to provide consistent and reproducible ways for machine-driven mapping of these gaps and a potential update of the existing ones. Finally, the performance analysis identifies the most important hyper-parameters, and provides hints on the models\u2019 deployment and their transferability.<\/jats:p>","DOI":"10.3390\/rs11080907","type":"journal-article","created":{"date-parts":[[2019,4,15]],"date-time":"2019-04-15T11:15:58Z","timestamp":1555326958000},"page":"907","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Evaluation of the Potential of Convolutional Neural Networks and Random Forests for Multi-Class Segmentation of Sentinel-2 Imagery"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2262-0580","authenticated-orcid":false,"given":"Vasileios","family":"Syrris","sequence":"first","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy"}]},{"given":"Paul","family":"Hasenohr","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy"}]},{"given":"Blagoj","family":"Delipetrev","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0411-741X","authenticated-orcid":false,"given":"Alexander","family":"Kotsev","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4317-4807","authenticated-orcid":false,"given":"Pieter","family":"Kempeneers","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8479-9205","authenticated-orcid":false,"given":"Pierre","family":"Soille","sequence":"additional","affiliation":[{"name":"European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0305-9006(03)00062-X","article-title":"Remote sensing for mapping and monitoring land-cover and land-use change","volume":"61","author":"Treitz","year":"2004","journal-title":"Prog. 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