A Robust Dual-Mode Machine Learning Framework for Classifying Deforestation Patterns in Amazon Native Lands

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFaculty of Basic Sciences (FBS)-
Autor(es): dc.creatorRodrigues, Julia-
Autor(es): dc.creatorDias, Mauricio Araújo-
Autor(es): dc.creatorNegri, Rogério-
Autor(es): dc.creatorHussain, Sardar Muhammad-
Autor(es): dc.creatorCasaca, Wallace-
Data de aceite: dc.date.accessioned2025-08-21T16:28:51Z-
Data de disponibilização: dc.date.available2025-08-21T16:28:51Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-09-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/land13091427-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/301307-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/301307-
Descrição: dc.descriptionThe integrated use of remote sensing and machine learning stands out as a powerful and well-established approach for dealing with various environmental monitoring tasks, including deforestation detection. In this paper, we present a tunable, data-driven methodology for assessing deforestation in the Amazon biome, with a particular focus on protected conservation reserves. In contrast to most existing works from the specialized literature that typically target vast forest regions or privately used lands, our investigation concentrates on evaluating deforestation in particular, legally protected areas, including indigenous lands. By integrating the open data and resources available through the Google Earth Engine, our framework is designed to be adaptable, employing either anomaly detection methods or artificial neural networks for classifying deforestation patterns. A comprehensive analysis of the classifiers’ accuracy, generalization capabilities, and practical usage is provided, with a numerical assessment based on a case study in the Amazon rainforest regions of São Félix do Xingu and the Kayapó indigenous reserve.-
Descrição: dc.descriptionSão Paulo State University (UNESP) Institute of Biosciences Humanities and Exact Sciences (IBILCE)-
Descrição: dc.descriptionSão Paulo State University (UNESP) Faculty of Science and Technology (FCT)-
Descrição: dc.descriptionSão Paulo State University (UNESP) Science and Technology Institute (ICT)-
Descrição: dc.descriptionBalochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Faculty of Basic Sciences (FBS)-
Descrição: dc.descriptionSão Paulo State University (UNESP) Institute of Biosciences Humanities and Exact Sciences (IBILCE)-
Descrição: dc.descriptionSão Paulo State University (UNESP) Faculty of Science and Technology (FCT)-
Descrição: dc.descriptionSão Paulo State University (UNESP) Science and Technology Institute (ICT)-
Idioma: dc.languageen-
Relação: dc.relationLand-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectanomaly detection-
Palavras-chave: dc.subjectGoogle Earth Engine-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectneural networks-
Título: dc.titleA Robust Dual-Mode Machine Learning Framework for Classifying Deforestation Patterns in Amazon Native Lands-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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