Unsupervised Change Detection Methods Applied to Landslide Mapping: Case Study in São Sebastião, Brazil

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorNational Center for Monitoring and Early Warning of Natural Disasters-
Autor(es): dc.creatorMoço, Gabriella Almeida-
Autor(es): dc.creatorNegri, Rogério Galante-
Autor(es): dc.creatorPaumpuch, Luana Albertani-
Autor(es): dc.creatorRibeiro, João Vitor Mariano-
Autor(es): dc.creatorBressane, Adriano-
Autor(es): dc.creatorBortolozo, Cassiano-
Data de aceite: dc.date.accessioned2025-08-21T17:46:01Z-
Data de disponibilização: dc.date.available2025-08-21T17:46:01Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1111/tgis.13256-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308213-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308213-
Descrição: dc.descriptionLandslides represent a growing global geological hazard, further intensified by climate-induced changes. Remote sensing data, through its capacity for repetitive collection and change detection techniques, that compare and quantify the spatio-temporal alterations over time, plays a critical role in landslide detection. Considering the February 2023 São Sebastião event and Sentinel-2 imagery, we assessed diverse unsupervised change detection techniques, encompassing both traditional and recent machine learning-based approaches. Notably, the Floating References (FR) and Homogeneous Blocks Single-class Classification (HBSC) methods outperform classic approaches and deliver the most accurate results with F1-Score and kappa coefficient exceeding 0.96 and 0.92, respectively. These outcomes demonstrate the efficacy of machine learning in automating landslide delineation and underscore the necessity of meticulous data and parameter selection in achieving high-accuracy automatic landslide mapping. Lastly, this study fills a significant gap in the existing literature by evaluating unsupervised change detection methods for landslide mapping within the Brazilian context.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionInstitute of Science and Technology São Paulo State University-
Descrição: dc.descriptionBrazilian Center for EarlyWarning and Monitoring for Natural Disasters Graduate Program in Natural Disasters São Paulo State University-
Descrição: dc.descriptionGraduate Program in Civil and Environmental Engineering São Paulo State University-
Descrição: dc.descriptionNational Center for Monitoring and Early Warning of Natural Disasters-
Descrição: dc.descriptionInstitute of Science and Technology São Paulo State University-
Descrição: dc.descriptionBrazilian Center for EarlyWarning and Monitoring for Natural Disasters Graduate Program in Natural Disasters São Paulo State University-
Descrição: dc.descriptionGraduate Program in Civil and Environmental Engineering São Paulo State University-
Descrição: dc.descriptionCNPq: 305220/2022-5-
Descrição: dc.descriptionCNPq: 383480/2023-0-
Formato: dc.format2626-2638-
Idioma: dc.languageen-
Relação: dc.relationTransactions in GIS-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectdigital image analysis-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectmultispectral-
Palavras-chave: dc.subjectremote sensing-
Palavras-chave: dc.subjectSentinel-2-
Título: dc.titleUnsupervised Change Detection Methods Applied to Landslide Mapping: Case Study in São Sebastião, Brazil-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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