Prof Dr Gilberto Camara
This workshop provides a hands-on introduction to sits, an open-source R package for satellite image time series analysis using machine learning. The package supports the complete cycle of data analysis for land classification, including data management, validation and quality assessment, filtering, classification, post-processing, and accuracy estimates. Its API provides a simple but powerful set of functions. The package works with analysis-ready data collections available in different cloud services such as AWS, Microsoft, and Digital Earth Africa. It supports a large number of machine learning and deep learning techniques, such as random forests, support vector machines, extreme gradient boosting, multi-layer perceptrons and 1D convolutional neural networks. It includes methods for sample quality control, model validation and accuracy assessment. The software is capable of processing large data sets (100-500 million ha) in an efficient matter. Software and documentation are available at github (https://github.com/e-sensing/sits).
Don't forget to register before we run out of tickets!