FOREST SEMANTIC SEGMENTATION BASED ON DEEP LEARNING USING SENTINEL-2 IMAGES

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dc.authorscopusid58939729800en_US
dc.authorscopusid58939946900en_US
dc.authorscopusid58940052400en_US
dc.authorscopusid57772011400en_US
dc.authorscopusid56247265800en_US
dc.authorscopusid36460530900en_US
dc.authorscopusid15130508500en_US
dc.contributor.authorHizal, C.
dc.contributor.authorGulsu, G.
dc.contributor.authorAkgun, H. Y.
dc.contributor.authorKulavuz, B.
dc.contributor.authorBakirman, T.
dc.contributor.authorAydin, A.
dc.contributor.authorBayram, B.
dc.date.accessioned2024-08-23T16:03:24Z
dc.date.available2024-08-23T16:03:24Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description8th International Conference on GeoInformation Advances (GeoAdvances) -- JAN 11-12, 2024 -- Istanbul, TURKEYen_US
dc.description.abstractForests are invaluable for maintaining biodiversity, watersheds, rainfall levels, bioclimatic stability, carbon sequestration and climate change mitigation, and the sustainability of large-scale climate regimes. In other words, forests provide a wide range of ecosystem services and livelihoods for the people and play a critical role in influencing global atmospheric cycles. Providing sustainable, reliable, and accurate information on forest cover change is essential for an holistic forest management, efficient use of resources, neutralizing the effects of global warming and better monitoring of deforestation activities. Within the scope of this study, it is aimed to perform semantic segmentation of 5 different tree species (larch, red pine, yellow pine, oak, spruce) from Sentinel-2 satellite images. For this purpose, the regions where these tree species are densely populated in Turkey (Marmara, Aegean, Eastern Black Sea) were selected as pilot regions. A unique data set was created using the data of the selected pilot regions. As a result of the study, it was possible to determine the forest types temporally for the selected classes with more than 90% Intersection over Union score for all classes. The developed deep learning model with the created forest data set can be implemented to the other forests areas with same species in other parts of the world.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [2209-B, 1139B412200297]en_US
dc.description.sponsorshipThis study has been supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under 2209-B project number: 1139B412200297.en_US
dc.identifier.doi10.5194/isprs-archives-XLVIII-4-W9-2024-229-2024
dc.identifier.endpage236en_US
dc.identifier.issn1682-1750
dc.identifier.issn2194-9034
dc.identifier.scopus2-s2.0-85187776487en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage229en_US
dc.identifier.urihttps://doi.org/10.5194/isprs-archives-XLVIII-4-W9-2024-229-2024
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13734
dc.identifier.wosWOS:001234953400030en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherCopernicus Gesellschaft Mbhen_US
dc.relation.ispartof8th International Conference on Geoinformation Advances, Geoadvances 2024, Vol. 48-4en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep Learningen_US
dc.subjectSemantic Segmentationen_US
dc.subjectSentinel-2en_US
dc.subjectRemote Sensingen_US
dc.subjectStand Mapen_US
dc.subjectTree Species Compositionen_US
dc.subjectNeural-Networksen_US
dc.subjectVegetationen_US
dc.subjectClassificationen_US
dc.subjectIndexen_US
dc.subjectUrbanen_US
dc.titleFOREST SEMANTIC SEGMENTATION BASED ON DEEP LEARNING USING SENTINEL-2 IMAGESen_US
dc.typeConference Objecten_US

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