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Machine Learning e conservação da Amazônia: uma revisão sobre o uso de Machine Learning na conservação da região da Amazônia
(Centro Universitário do Estado do Pará, 2023-12-07) Dias, Carlos Eduardo Nylander Bitencourt; Barros, Rafael Luís Carvalho; Gomes, Vitor Hugo Freitas; Elgrably, Isaac Souza; http://lattes.cnpq.br/7590598824563858; http://lattes.cnpq.br/5218954387107307; Nascimento, Polyana Santos Fonseca; http://lattes.cnpq.br/6889523334917369; Araújo, Fábio Rocha de; http://lattes.cnpq.br/2407240421934932
With an approximated expanse of 6 million km², the Amazon Rainforest is a region of global interest, particularly for its great biodiversity some of which still unmeasured. The forest exercises an important part in regional and global climate control, capturing and storing CO2, contributing with rain formation and varied biogeochemical cycles. Its large contribution to the livelihood of native and traditional communities is not to be neglected. Despite it all, the territory faces great challenges, suffering with the impacts of deforestation, fires, global climate change, pollution, and more. In its conservation efforts, the usage of advancing tools and technology has resulted in the implementation of Artificial Intelligence and its sub-areas, such as Machine Learning, which has contributed to the development of predictive studies in the Amazon. Here, we revised studies that implemented Machine Learning in the conservation of Amazon’s territory, seeking a deeper understanding of its limitations and future usage of this technology. We concluded that, as a technology Machine Learning has helped in preservation efforts, but there still is much that can be done to improve its usage, such as the utilization of comprehensive data, training of professionals and experts to adequately implement the technology and to analyze the results, being these improvements crucial to the futureconservation efforts in the Amazon Region.