Evaluation of semantic segmentation methods for deforestation detection in the amazon

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dc.identifier.uri http://dx.doi.org/10.15488/10818
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/10896
dc.contributor.author Andrade, R.B.
dc.contributor.author Costa, G.A.O.P.
dc.contributor.author Mota, G.L.A.
dc.contributor.author Ortega, M.X.
dc.contributor.author Feitosa, R.Q.
dc.contributor.author Soto, P.J.
dc.contributor.author Heipke, Christian
dc.contributor.editor Paparoditis, N.
dc.contributor.editor Mallet, C.
dc.contributor.editor Lafarge, F.
dc.date.accessioned 2021-04-27T08:35:57Z
dc.date.available 2021-04-27T08:35:57Z
dc.date.issued 2020
dc.identifier.citation Andrade, R.B.; Costa, G.A.O.P.; Mota, G.L.A.; Ortega, M.X.; Feitosa, R.Q. et al.: Evaluation of semantic segmentation methods for deforestation detection in the amazon. In: Paparoditis, N. et al. (Eds.): XXIV ISPRS Congress, Commission III : edition 2020. Katlenburg-Lindau : Copernicus Publications, 2020. (ISPRS Archives ; 43,B3), S. 1497-1505. DOI: https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1497-2020
dc.description.abstract Deforestation is a wide-reaching problem, responsible for serious environmental issues, such as biodiversity loss and global climate change. Containing approximately ten percent of all biomass on the planet and home to one tenth of the known species, the Amazon biome has faced important deforestation pressure in the last decades. Devising efficient deforestation detection methods is, therefore, key to combat illegal deforestation and to aid in the conception of public policies directed to promote sustainable development in the Amazon. In this work, we implement and evaluate a deforestation detection approach which is based on a Fully Convolutional, Deep Learning (DL) model: the DeepLabv3+. We compare the results obtained with the devised approach to those obtained with previously proposed DL-based methods (Early Fusion and Siamese Convolutional Network) using Landsat OLI-8 images acquired at different dates, covering a region of the Amazon forest. In order to evaluate the sensitivity of the methods to the amount of training data, we also evaluate them using varying training sample set sizes. The results show that all tested variants of the proposed method significantly outperform the other DL-based methods in terms of overall accuracy and F1-score. The gains in performance were even more substantial when limited amounts of samples were used in training the evaluated methods. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. eng
dc.language.iso eng
dc.publisher Katlenburg-Lindau : Copernicus Publications
dc.relation.ispartof XXIV ISPRS Congress, Commission III : edition 2020
dc.relation.ispartofseries ISPRS Archives ; 43,B3
dc.rights CC BY 4.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Amazon forest eng
dc.subject change detection eng
dc.subject deep learning eng
dc.subject DeepLabv3+ eng
dc.subject deforestation eng
dc.subject semantic segmentation eng
dc.subject.classification Konferenzschrift ger
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Evaluation of semantic segmentation methods for deforestation detection in the amazon
dc.type BookPart
dc.type Text
dc.relation.essn 2194-9034
dc.relation.issn 1682-1750
dc.relation.doi https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1497-2020
dc.bibliographicCitation.issue B3
dc.bibliographicCitation.volume 43
dc.bibliographicCitation.firstPage 1497
dc.bibliographicCitation.lastPage 1505
dc.description.version publishedVersion
tib.accessRights frei zug�nglich

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