Environmental monitoring using drone images and convolutional neural networks

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorSaõ Paulo State University-
Autor(es): dc.contributorCorumbá Concessões S.A-
Autor(es): dc.creatorThomazella, R.-
Autor(es): dc.creatorCastanho, J. E.-
Autor(es): dc.creatorDotto, F. R.L.-
Autor(es): dc.creatorRodrigues Júnior, O. P.-
Autor(es): dc.creatorRosa, G. H.-
Autor(es): dc.creatorMarana, A. N.-
Autor(es): dc.creatorPapa, J. P.-
Data de aceite: dc.date.accessioned2025-08-21T20:05:54Z-
Data de disponibilização: dc.date.available2025-08-21T20:05:54Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2018-10-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/IGARSS.2018.8518581-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/231431-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/231431-
Descrição: dc.descriptionRecently, 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.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment of Electrical Engineering Faculty of Engineering of Bauru Saõ Paulo State University-
Descrição: dc.descriptionCorumbá Concessões S.A, SIA Trecho 3 Lote 1875-
Descrição: dc.descriptionDepartment of Computing Faculty of Sciences Saõ Paulo State University-
Descrição: dc.descriptionCNPq: 306166/2014-3-
Formato: dc.format8941-8944-
Idioma: dc.languageen-
Relação: dc.relationInternational Geoscience and Remote Sensing Symposium (IGARSS)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectConvolutional Neural Networks-
Palavras-chave: dc.subjectDrones-
Palavras-chave: dc.subjectLand-use classification-
Título: dc.titleEnvironmental monitoring using drone images and convolutional neural networks-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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