Um sistema WebGIS para classificação supervisionada de cobertura do solo utilizando inteligência artificial

• Ano de defesa: 2022
• Autor(a) principal: Fernandes, Yuri Kuivjogi
• Orientador(a): Costa, Ronaldo Martins da
• Banca de defesa: Costa, Ronaldo Martins da, Oliveira, Bruna Mendes de, Cremon, Édipo Henrique
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RMG)
• País: Brasil
• Palavras-chave em Português: Sensoriamento remoto; Identificação espectral; Inteligência artificial; Processamento em nuvem; Culturas agrícolas
• Palavras-chave em Inglês: Remote sensing; Spectral identification; Artificial intelligence; Cloud processing; Agricultural crops
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/12519

With the advancement in data generation for Earth observation and its availability free of charge, the Remote Sensing (SR) area advanced significantly. Over the years, it has been observed the migration of RS applications to the internet environment, facilitating searches of different uses. This work proposes a new approach for collecting and manipulating spatial data for spectral classification based on pixels. A web application was built integrating Google Earth Engine, Google Maps and Auto Machine Learning services for performance analysis. Experiments using samples from land cover regions in Goiás, Brazil, justifying the gain in time, processing and data storage. Such contributions are related to the large amount of information from satellite images collected in a conventional way, which are later not used. As a final result, there is an image classified through the classification process representing the different land cover classes. Model training achieved an accuracy of 99.85% using the Light Gradient Boosting Machine (LightGBM) model. In addition to these benefits, the optimization of processes allows the inclusion of research from other major areas, thus for the greater dissemination of knowledge in the area of SR and pattern recognition applications.