Gilberto Camara
Researcher in GIScience, Geoinformatics, Spatial Data Science and Land Use Change at Brazil’s National Institute for Space Research (INPE). Former director of INPE (2005-2012). Director of the Secretariat of GEO (Group on Earth Observations) from 2018 to 2012.
Sessions
This talk describes an open-source R package for satellite image time series analysis using machine learning. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments, including AWS, MS-Azure, and Digital Earth Africa. In sits, satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. The package is available on https://github.com/e-sensing/sits and the documentation is available on https://e-sensing.github.io/sitsbook/.