Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorGino, Vinicius L. S.-
Autor(es): dc.creatorNegri, Rogerio G.-
Autor(es): dc.creatorSouza, Felipe N.-
Autor(es): dc.creatorSilva, Erivaldo A.-
Autor(es): dc.creatorBressane, Adriano-
Autor(es): dc.creatorMendes, Tatiana S. G.-
Autor(es): dc.creatorCasaca, Wallace-
Data de aceite: dc.date.accessioned2025-08-21T22:18:40Z-
Data de disponibilização: dc.date.available2025-08-21T22:18:40Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/su15064725-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/245606-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/245606-
Descrição: dc.descriptionThe synergistic use of remote sensing and unsupervised machine learning has emerged as a potential tool for addressing a variety of environmental monitoring applications, such as detecting disaster-affected areas and deforestation. This paper proposes a new machine-intelligent approach to detecting and characterizing spatio-temporal changes on the Earth's surface by using remote sensing data and unsupervised learning. Our framework was designed to be fully automatic by integrating unsupervised anomaly detection models, remote sensing image series, and open data extracted from the Google Earth Engine platform. The methodology was evaluated by taking both simulated and real-world environmental data acquired from several imaging sensors, including Landsat-8 OLI, Sentinel-2 MSI, and Terra MODIS. The experimental results were measured with the kappa and F1-score metrics, and they indicated an assertiveness level of 0.85 for the change detection task, demonstrating the accuracy and robustness of the proposed approach when addressing distinct environmental monitoring applications, including the detection of disaster-affected areas and deforestation mapping.-
Descrição: dc.descriptionFunda��o de Amparo � Pesquisa do Estado de S�o Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Cient�fico e Tecnol�gico (CNPq)-
Descrição: dc.descriptionSao Paulo State University (UNESP)-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Sci & Technol Inst ICT, BR-12245000 Sao Jose Dos Campos, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Fac Sci & Technol FCT, BR-19060080 Presidente Prudente, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Inst Biosci Letters & Exact Sci IBILCE, BR-15054000 Sao Jose Do Rio Preto, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Sci & Technol Inst ICT, BR-12245000 Sao Jose Dos Campos, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Fac Sci & Technol FCT, BR-19060080 Presidente Prudente, Brazil-
Descrição: dc.descriptionSao Paulo State Univ UNESP, Inst Biosci Letters & Exact Sci IBILCE, BR-15054000 Sao Jose Do Rio Preto, Brazil-
Descrição: dc.descriptionFAPESP: 2021/01305-6-
Descrição: dc.descriptionFAPESP: 2021/03328-3-
Descrição: dc.descriptionCNPq: 316228/2021-4-
Formato: dc.format19-
Idioma: dc.languageen-
Publicador: dc.publisherMdpi-
Relação: dc.relationSustainability-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectanomaly detection-
Palavras-chave: dc.subjecttime series-
Palavras-chave: dc.subjectlandscape dynamics-
Palavras-chave: dc.subjectframework-
Título: dc.titleIntegrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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