ENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKS

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dc.contributor.authorThomazella, R. [UNESP]
dc.contributor.authorCastanho, J. E. [UNESP]
dc.contributor.authorDotto, F. R. L. [UNESP]
dc.contributor.authorRodrigues Junior, O. P.
dc.contributor.authorRosa, G. H. [UNESP]
dc.contributor.authorMarana, A. N. [UNESP]
dc.contributor.authorPapa, J. P. [UNESP]
dc.contributor.authorIEEE
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionCorumba Concessoes SA
dc.date.accessioned2019-10-04T12:32:39Z
dc.date.available2019-10-04T12:32:39Z
dc.date.issued2018-01-01
dc.description.abstractRecently, drone images have been used in a number of applications, mainly for pollution control and surveillance purposes. In this paper, we introduce the well-known Convolutional Neural Networks in the context of environmental monitoring using drone images, and we show their robustness in real-world images obtained from uncontrolled scenarios. We consider a transfer learning-based approach and compare two neural models, i.e., VGG16 and VGG19, to distinguish four classes: water, deforesting area, forest, and buildings. The results are analyzed by experts in the field and considered pretty much reasonable.en
dc.description.affiliationSao Paulo State Univ, Dept Elect Engn, Fac Engn Bauru, Sao Paulo, SP, Brazil
dc.description.affiliationCorumba Concessoes SA, SIA Trecho 3 Lote 1875 Sia Sul, BR-71200030 Brasilia, DF, Brazil
dc.description.affiliationSao Paulo State Univ, Dept Comp, Fac Sci, Sao Paulo, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Elect Engn, Fac Engn Bauru, Sao Paulo, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Comp, Fac Sci, Sao Paulo, SP, Brazil
dc.description.sponsorshipCorumba Concessoes S.A.
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIntel AI Academy
dc.description.sponsorshipIdCorumba Concessoes S.A.: ANEEL PD-2262-1602/2016
dc.description.sponsorshipIdCNPq: 306166/2014-3
dc.description.sponsorshipIdCNPq: 307066/2017-7
dc.description.sponsorshipIdFAPESP: 2013/07375-0
dc.description.sponsorshipIdFAPESP: 2014/12236-1
dc.description.sponsorshipIdFAPESP: 2015/25739-4
dc.description.sponsorshipIdFAPESP: 2016/19403-6
dc.description.sponsorshipIdIntel AI Academy: 2597.2017
dc.format.extent8941-8944
dc.identifier.citationIgarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium. New York: Ieee, p. 8941-8944, 2018.
dc.identifier.issn2153-6996
dc.identifier.urihttp://hdl.handle.net/11449/185094
dc.identifier.wosWOS:000451039808130
dc.language.isoeng
dc.publisherIeee
dc.relation.ispartofIgarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectLand-use classification
dc.subjectDrones
dc.subjectConvolutional Neural Networks
dc.titleENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKSen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee
unesp.author.lattes6027713750942689[6]
unesp.author.orcid0000-0003-4861-7061[6]

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