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Novel cluster modeling for the spatiotemporal analysis of coastal upwelling

2022-11Conference object Restricted access
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dc.contributor.authorNascimento, Susana
dc.contributor.authorMartins, Alexandre
dc.contributor.authorRelvas, Paulo
dc.contributor.authorLuis, Joaquim
dc.contributor.authorMirkin, Boris
dc.date.accessioned2023-04-12T08:55:26Z
dc.date.available2023-04-12T08:55:26Z
dc.date.issued2022-11
dc.description.abstractThis work proposes a spatiotemporal clustering approach for the analysis of coastal upwelling from Sea Surface Temperature (SST) grid maps derived from satellite images. The algorithm, Core-Shell clustering, models the upwelling as an evolving cluster whose core points are constant during a certain time window while the shell points move through an in-and-out binary sequence. The least squares minimization of clustering criterion allows to derive key parameters in an automated way. The algorithm is initialized with an extension of Seeded Region Growing offering self-tuning thresholding, the STSEC algorithm, that is able to precisely delineate the upwelling region at each SST instant map. Yet, the application of STSEC to the SST grid maps as temporal data puts the business of finding relatively stable "time windows", here called "time ranges", for obtaining the core clusters onto an automated footing. The experiments conducted with three yearly collections of SST data of the Portuguese coast shown that the core-shell clusters precisely recognize the upwelling regions taking as ground-truth the STSEC segmentations with Kulczynski similarity score values higher than 98%. Also, the extracted time series of upwelling features presented consistent regularities among the three independent upwelling seasons.pt_PT
dc.description.sponsorshipLA/P/0101/2020pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-031-16474-3_46pt_PT
dc.identifier.isbn978-3-031-16473-6
dc.identifier.urihttp://hdl.handle.net/10400.1/19438
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationNOVA Laboratory for Computer Science and Informatics
dc.relationInstituto Dom Luiz
dc.relationAlgarve Centre for Marine Sciences
dc.relationAlgarve Centre for Marine Sciences
dc.subjectSpatiotemporal clusteringpt_PT
dc.subjectSequential clusteringpt_PT
dc.subjectTime windowpt_PT
dc.subjectCoastal upwellingpt_PT
dc.titleNovel cluster modeling for the spatiotemporal analysis of coastal upwellingpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleNOVA Laboratory for Computer Science and Informatics
oaire.awardTitleInstituto Dom Luiz
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardTitleAlgarve Centre for Marine Sciences
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50019%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04326%2F2020/PT
oaire.citation.endPage574pt_PT
oaire.citation.startPage563pt_PT
oaire.citation.titleProgress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Sciencept_PT
oaire.citation.volume13566pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameRelvas
person.familyNameLuis
person.givenNamePaulo
person.givenNameJoaquim
person.identifier.ciencia-id2412-1F65-A044
person.identifier.ciencia-id0D11-8EF9-2E68
person.identifier.orcid0000-0002-6404-5895
person.identifier.orcid0000-0002-9035-4069
person.identifier.ridB-1257-2008
person.identifier.ridA-1112-2009
person.identifier.scopus-author-id6505976206
person.identifier.scopus-author-id7006391353
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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