ENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKS

Detalhes bibliográficos
Autor(a) principal: Thomazella, R. [UNESP]
Data de Publicação: 2018
Outros Autores: Castanho, J. E. [UNESP], Dotto, F. R. L. [UNESP], Rodrigues Junior, O. P., Rosa, G. H. [UNESP], Marana, A. N. [UNESP], Papa, J. P. [UNESP], IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/185094
Resumo: Recently, 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.
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spelling ENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKSLand-use classificationDronesConvolutional Neural NetworksRecently, 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.Corumba Concessoes S.A.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Intel AI AcademySao Paulo State Univ, Dept Elect Engn, Fac Engn Bauru, Sao Paulo, SP, BrazilCorumba Concessoes SA, SIA Trecho 3 Lote 1875 Sia Sul, BR-71200030 Brasilia, DF, BrazilSao Paulo State Univ, Dept Comp, Fac Sci, Sao Paulo, SP, BrazilSao Paulo State Univ, Dept Elect Engn, Fac Engn Bauru, Sao Paulo, SP, BrazilSao Paulo State Univ, Dept Comp, Fac Sci, Sao Paulo, SP, BrazilCorumba Concessoes S.A.: ANEEL PD-2262-1602/2016CNPq: 306166/2014-3CNPq: 307066/2017-7FAPESP: 2013/07375-0FAPESP: 2014/12236-1FAPESP: 2015/25739-4FAPESP: 2016/19403-6Intel AI Academy: 2597.2017IeeeUniversidade Estadual Paulista (Unesp)Corumba Concessoes SAThomazella, R. [UNESP]Castanho, J. E. [UNESP]Dotto, F. R. L. [UNESP]Rodrigues Junior, O. P.Rosa, G. H. [UNESP]Marana, A. N. [UNESP]Papa, J. P. [UNESP]IEEE2019-10-04T12:32:39Z2019-10-04T12:32:39Z2018-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject8941-8944Igarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium. New York: Ieee, p. 8941-8944, 2018.2153-6996http://hdl.handle.net/11449/185094WOS:000451039808130Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIgarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposiuminfo:eu-repo/semantics/openAccess2024-06-28T13:34:43Zoai:repositorio.unesp.br:11449/185094Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:06:04.442451Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv ENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKS
title ENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKS
spellingShingle ENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKS
Thomazella, R. [UNESP]
Land-use classification
Drones
Convolutional Neural Networks
title_short ENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKS
title_full ENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKS
title_fullStr ENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKS
title_full_unstemmed ENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKS
title_sort ENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKS
author Thomazella, R. [UNESP]
author_facet Thomazella, R. [UNESP]
Castanho, J. E. [UNESP]
Dotto, F. R. L. [UNESP]
Rodrigues Junior, O. P.
Rosa, G. H. [UNESP]
Marana, A. N. [UNESP]
Papa, J. P. [UNESP]
IEEE
author_role author
author2 Castanho, J. E. [UNESP]
Dotto, F. R. L. [UNESP]
Rodrigues Junior, O. P.
Rosa, G. H. [UNESP]
Marana, A. N. [UNESP]
Papa, J. P. [UNESP]
IEEE
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Corumba Concessoes SA
dc.contributor.author.fl_str_mv Thomazella, R. [UNESP]
Castanho, J. E. [UNESP]
Dotto, F. R. L. [UNESP]
Rodrigues Junior, O. P.
Rosa, G. H. [UNESP]
Marana, A. N. [UNESP]
Papa, J. P. [UNESP]
IEEE
dc.subject.por.fl_str_mv Land-use classification
Drones
Convolutional Neural Networks
topic Land-use classification
Drones
Convolutional Neural Networks
description Recently, 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.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
2019-10-04T12:32:39Z
2019-10-04T12:32:39Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv Igarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium. New York: Ieee, p. 8941-8944, 2018.
2153-6996
http://hdl.handle.net/11449/185094
WOS:000451039808130
identifier_str_mv Igarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium. New York: Ieee, p. 8941-8944, 2018.
2153-6996
WOS:000451039808130
url http://hdl.handle.net/11449/185094
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Igarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 8941-8944
dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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