Full metadata record
DC FieldValueLanguage
dc.contributor.authorSYRRIS VASILEIOSen_GB
dc.contributor.authorHASENOHR PAULen_GB
dc.contributor.authorDELIPETREV BLAGOJen_GB
dc.contributor.authorKOTSEV ALEXANDERen_GB
dc.contributor.authorKEMPENEERS PIETERen_GB
dc.contributor.authorSOILLE PIERREen_GB
dc.date.accessioned2019-05-01T00:04:30Z-
dc.date.available2019-04-24en_GB
dc.date.available2019-05-01T00:04:30Z-
dc.date.created2019-04-15en_GB
dc.date.issued2019en_GB
dc.date.submitted2019-03-27en_GB
dc.identifier.citationREMOTE SENSING vol. 11 no. 8 p. 907en_GB
dc.identifier.issn2072-4292 (online)en_GB
dc.identifier.urihttps://www.mdpi.com/2072-4292/11/8/907en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC116316-
dc.description.abstractMotivated 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’s 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’ deployment and their transferabilityen_GB
dc.description.sponsorshipJRC.I.3-Text and Data Miningen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherMDPIen_GB
dc.relation.ispartofseriesJRC116316en_GB
dc.titleEvaluation of the potential of convolutional neural networks and random forests for multi-class segmentation of Sentinel-2 imageryen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.3390/rs11080907 (online)en_GB
JRC Directorate:Joint Research Centre Corporate Activities

Files in This Item:
There are no files associated with this item.


Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.