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dc.contributor.authorArellano-Verdejo, J.
dc.contributor.authorLazcano-Hernandez, H.E.
dc.contributor.authorCabanillas-Terán, N.
dc.coverage.spatialCaribbean Seaen_US
dc.coverage.spatialMexicoen_US
dc.date.accessioned2020-04-25T15:36:05Z
dc.date.available2020-04-25T15:36:05Z
dc.date.issued2019
dc.identifier.citationArellano-Verdejo, J,; Lazcano-Hernandez, H.E. and Cabanillas-Terán, N. (2019) ERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean. PeerJ, 7:e6842 DOI: http://doi.org/10.7717/peerj.6842en_US
dc.identifier.urihttp://hdl.handle.net/11329/1300
dc.identifier.urihttp://dx.doi.org/10.25607/OBP-808
dc.description.abstractRecently, Caribbean coasts have experienced atypical massive arrivals of pelagic Sargassum with negative consequences both ecologically and economically. Based on deep learning techniques, this study proposes a novel algorithm for floating and accumulated pelagic Sargassum detection along the coastline of Quintana Roo, Mexico. Using convolutional and recurrent neural networks architectures, a deep neural network (named ERISNet) was designed specifically to detect these macroalgae along the coastline through remote sensing support. A new dataset which includes pixel values with and without Sargassum was built to train and test ERISNet. Aqua-MODIS imagery was used to build the dataset. After the learning process, the designed algorithm achieves a 90% of probability in its classification skills. ERISNet provides a novel insight to detect accurately algal blooms arrivals.en_US
dc.language.isoenen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.otherRemote sensingen_US
dc.subject.otherSatellite sensingen_US
dc.subject.otherNeural networksen_US
dc.subject.otherAlgal bloomsen_US
dc.subject.otherSargassumen_US
dc.subject.otherSeaweeden_US
dc.subject.otherDeep learningen_US
dc.subject.otherMacroalgaeen_US
dc.subject.otherManagement
dc.subject.otherManagement
dc.titleERISNet: deep neural network for Sargassum detection along the coastline of the Mexican Caribbean.en_US
dc.typeJournal Contributionen_US
dc.description.refereedRefereeden_US
dc.format.pagerange19pp.en_US
dc.identifier.doihttp://doi.org/10.7717/peerj.6842
dc.subject.parameterDisciplineParameter Discipline::Biological oceanography::Macroalgae and seagrassen_US
dc.subject.instrumentTypeMODISen_US
dc.bibliographicCitation.titlePeerJen_US
dc.bibliographicCitation.volume7en_US
dc.bibliographicCitation.issueArticle e6842en_US
dc.description.sdg14.2en_US
dc.description.eovMacroalgal canopy cover and compositionen_US
dc.description.maturitylevelTRL 8 Actual system completed and "mission qualified" through test and demonstration in an operational environment (ground or space)en_US
dc.description.bptypeBest Practiceen_US
dc.description.bptypeManual (incl. handbook, guide, cookbook etc)en_US
obps.contact.contactnameJavier Arellano-Verdejo
obps.contact.contactemailjavier.arellano@mail.ecosur.mx
obps.resourceurl.publisherhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6500371/pdf/peerj-07-6842.pdfen_US


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International