Remote Sensing and Machine Learning on Anomaly Detection at high spectral and temporal dynamics regions in Brazil

Detalhes bibliográficos
Autor(a) principal: Gino, Vinícius L.S. [UNESP]
Data de Publicação: 2022
Outros Autores: Negri, Rogério G. [UNESP], Souza, Felipe N. [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/247350
Resumo: In climate changes context Remote Sensing tools are widely used and widespread in research. In this sense, Artificial Intelligence rises offering possible improves for environmental monitoring applications using techniques such as Machine Learning for Anomaly Detection applied to Remote Sensing imagery to identify the spatio-temporal changes over the Earth’s surface. This approach is explored in three high dynamic regions in Brazil assessing deforestation, fires and technological disaster areas using One-Class SVM and Isolation Forest methods over MODIS, Landsat and Sentinel images.
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spelling Remote Sensing and Machine Learning on Anomaly Detection at high spectral and temporal dynamics regions in BrazilIn climate changes context Remote Sensing tools are widely used and widespread in research. In this sense, Artificial Intelligence rises offering possible improves for environmental monitoring applications using techniques such as Machine Learning for Anomaly Detection applied to Remote Sensing imagery to identify the spatio-temporal changes over the Earth’s surface. This approach is explored in three high dynamic regions in Brazil assessing deforestation, fires and technological disaster areas using One-Class SVM and Isolation Forest methods over MODIS, Landsat and Sentinel images.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Instituto de Ciência e Tecnologia (ICT) Universidade Estadual Paulista “Júlio de Mesquita Filho” (UNESP), SPInstituto de Ciência e Tecnologia (ICT) Universidade Estadual Paulista “Júlio de Mesquita Filho” (UNESP), SPFAPESP: 2018/01033-3FAPESP: 2020/14664-1FAPESP: 2021/01305-6Universidade Estadual Paulista (UNESP)Gino, Vinícius L.S. [UNESP]Negri, Rogério G. [UNESP]Souza, Felipe N. [UNESP]2023-07-29T13:13:44Z2023-07-29T13:13:44Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject99-110Proceedings of the Brazilian Symposium on GeoInformatics, p. 99-110.2179-4847http://hdl.handle.net/11449/2473502-s2.0-85159076324Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the Brazilian Symposium on GeoInformaticsinfo:eu-repo/semantics/openAccess2023-07-29T13:13:44Zoai:repositorio.unesp.br:11449/247350Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:08:26.987348Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Remote Sensing and Machine Learning on Anomaly Detection at high spectral and temporal dynamics regions in Brazil
title Remote Sensing and Machine Learning on Anomaly Detection at high spectral and temporal dynamics regions in Brazil
spellingShingle Remote Sensing and Machine Learning on Anomaly Detection at high spectral and temporal dynamics regions in Brazil
Gino, Vinícius L.S. [UNESP]
title_short Remote Sensing and Machine Learning on Anomaly Detection at high spectral and temporal dynamics regions in Brazil
title_full Remote Sensing and Machine Learning on Anomaly Detection at high spectral and temporal dynamics regions in Brazil
title_fullStr Remote Sensing and Machine Learning on Anomaly Detection at high spectral and temporal dynamics regions in Brazil
title_full_unstemmed Remote Sensing and Machine Learning on Anomaly Detection at high spectral and temporal dynamics regions in Brazil
title_sort Remote Sensing and Machine Learning on Anomaly Detection at high spectral and temporal dynamics regions in Brazil
author Gino, Vinícius L.S. [UNESP]
author_facet Gino, Vinícius L.S. [UNESP]
Negri, Rogério G. [UNESP]
Souza, Felipe N. [UNESP]
author_role author
author2 Negri, Rogério G. [UNESP]
Souza, Felipe N. [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Gino, Vinícius L.S. [UNESP]
Negri, Rogério G. [UNESP]
Souza, Felipe N. [UNESP]
description In climate changes context Remote Sensing tools are widely used and widespread in research. In this sense, Artificial Intelligence rises offering possible improves for environmental monitoring applications using techniques such as Machine Learning for Anomaly Detection applied to Remote Sensing imagery to identify the spatio-temporal changes over the Earth’s surface. This approach is explored in three high dynamic regions in Brazil assessing deforestation, fires and technological disaster areas using One-Class SVM and Isolation Forest methods over MODIS, Landsat and Sentinel images.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
2023-07-29T13:13:44Z
2023-07-29T13:13:44Z
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 Proceedings of the Brazilian Symposium on GeoInformatics, p. 99-110.
2179-4847
http://hdl.handle.net/11449/247350
2-s2.0-85159076324
identifier_str_mv Proceedings of the Brazilian Symposium on GeoInformatics, p. 99-110.
2179-4847
2-s2.0-85159076324
url http://hdl.handle.net/11449/247350
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings of the Brazilian Symposium on GeoInformatics
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 99-110
dc.source.none.fl_str_mv Scopus
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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