Um sistema WebGIS para classificação supervisionada de cobertura do solo utilizando inteligência artificial
Nenhuma Miniatura disponível
Data
2022-10-21
Autores
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Federal de Goiás
Resumo
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.
Descrição
Citação
FERNANDES, Y. K. Um sistema WebGIS para classificação supervisionada de cobertura do solo utilizando inteligência artificial. 2022. 67 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2022.