Neuroevolution-based Classifiers for Deforestation... - BV FAPESP
Busca avançada
Ano de início
Entree


Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests

Texto completo
Autor(es):
Pimenta, Guilherme B. A. ; Dallaqua, Fernanda B. J. R. ; Fazenda, Alvaro ; Faria, Fabio A. ; DeCarvalho, BM ; Goncalves, LMG
Número total de Autores: 6
Tipo de documento: Artigo Científico
Fonte: 2022 35TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2022); v. N/A, p. 6-pg., 2022-01-01.
Resumo

Tropical forests represent the home of many species on the planet for flora and fauna, retaining billions of tons of carbon footprint, promoting clouds and rain formation, implying a crucial role in the global ecosystem, besides representing the home to countless indigenous peoples. Unfortunately, millions of hectares of tropical forests are lost every year due to deforestation or degradation. To mitigate that fact, monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals. These monitoring/detection programs generally use remote sensing images, image processing techniques, machine learning methods, and expert photointerpretation to analyze, identify and quantify possible changes in forest cover. Several projects have proposed different computational approaches, tools, and models to efficiently identify recent deforestation areas, improving deforestation monitoring programs in tropical forests. In this sense, this paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks. Furthermore, a novel framework called e-NEAT has been created and achieved classification results above 90% for balanced accuracy measure in the target application using an extremely reduced and limited training set for learning the classification models. These results represent a relative gain of 6.2% over the best baseline ensemble method compared in this paper. (AU)

Processo FAPESP: 19/26702-8 - Tendências em computação de alto desempenho, do gerenciamento de recursos a novas arquiteturas de computadores
Beneficiário:Alfredo Goldman vel Lejbman
Modalidade de apoio: Auxílio à Pesquisa - Temático
Processo FAPESP: 15/24485-9 - Internet do futuro aplicada a cidades inteligentes
Beneficiário:Fabio Kon
Modalidade de apoio: Auxílio à Pesquisa - Temático
Processo FAPESP: 14/50937-1 - INCT 2014: da Internet do Futuro
Beneficiário:Fabio Kon
Modalidade de apoio: Auxílio à Pesquisa - Temático
Processo FAPESP: 18/23908-1 - Buscando Robustez em Arquiteturas de Aprendizagem Profunda para Aplicações e-Science
Beneficiário:Fabio Augusto Faria
Modalidade de apoio: Bolsas no Exterior - Pesquisa
Processo FAPESP: 17/25908-6 - Aprendizado fracamente supervisionado para análise de vídeos no domínio comprimido em tarefas de recuperação e classificação para alertas visuais
Beneficiário:João Paulo Papa
Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE