Utilização de inteligência artificial para a detecção de plantações em imagens de satélite

Detalhes bibliográficos
Ano de defesa: 2022
Autor(a) principal: Jacques, Matheus Mello
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Santa Maria
Brasil
Engenharia Elétrica
UFSM
Programa de Pós-Graduação em Engenharia Elétrica
Centro de Tecnologia
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://repositorio.ufsm.br/handle/1/28173
Resumo: Loss identification is one of the main challenges for electric utility companies today. The loss factor in Brazil was approximately 14% in 2018, where 6.6% are non-technical losses totaling a cost of almost R$5 billion per year. This work proposes the location of thefts in the electrical network using satellite images. For this, Deep Learning models are used to identify rice crops and later this data feeds another artificial intelligence algorithm that analyzes whether such consumer unit is generating non-technical losses. This work, therefore, proposes the use of the topology proposed by (??) for the classification of plantations in the Uruguaiana region in Rio Grande do Sul. Sentinel-2 satellite images were acquired by the (ESA, 2021) platform, processed and used as input to the neural network. In addition, they were also used for the creation of the labels, through the use of the Maximum Likelihood method combined with the visual inspection of a professional. After training the neural network for 30 epochs, we found a loss function close to 0.3 in the test dataset, an accuracy of 90% and a Jaccard Index of 68.84%. Through the application of this Deep Learning model, it was possible to detect and classify the plantations, to then cross-reference with data from the electrical network to identify possible cases of losses.