A Fully Automatic, Interpretable and Adaptive Machine Learning Approach to Map Burned Area from Remote Sensing

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datacite.relatedItem.firstPage546
datacite.relatedItem.issue8
datacite.relatedItem.relatedIdentifierTypeISSN
datacite.relatedItem.relatedItemIdentifier2220-9964
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datacite.relatedItem.titleIJGI
datacite.relatedItem.volume10
dc.contributor.authorDaniela Stroppiana
dc.contributor.authorGloria Bordogna
dc.contributor.authorMatteo Sali
dc.contributor.authorMirco Boschetti
dc.contributor.authorGiovanna Sona
dc.contributor.authorPietro Alessandro Brivio
dc.date.accessioned2025-05-14T12:53:50Z
dc.date.available2025-05-14T12:53:50Z
dc.date.issued2021
dc.identifier.doihttps://doi.org/10.3390/ijgi10080546
dc.identifier.otherjz000145-0542
dc.identifier.urihttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/21954
dc.publisherMDPI AG
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc550
dc.titleA Fully Automatic, Interpretable and Adaptive Machine Learning Approach to Map Burned Area from Remote Sensing
dc.typeArticle
dcat.distribution.pdfhttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/21955
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