Would you like to increase your skills in the field of satellite images processing using Machine Learning? Do you know the basics of GIS (geographic information system) or QGIS (an open-source application to view, edit and analyze geospatial data)? Then please feel free to join this workshop! It introduces how classification of satellite imagery can be done with QGIS by showing how to retrieve, process and classify satellite imagery, as well as how to assess performance of machine learning algorithms through error matrix and accuracy indexes.
The workshop involves two QGIS plugins: Semi-automatic Classification Plugin (SCP) and dzetsaka. SCP is used for majority of preprocessing operations such as retrieval of the Sentinel 2 imagery for an area of interest, DOS (Dark object subtraction) atmospheric correction, selection of specific bands for classification, creation of composite and computation of band algebra (i.e., Normalized Difference Vegetation Index (NDVI). The dzetsaka plugin is used to detect and classify built-up areas starting from preprocessed satellite imagery with Gaussian Mixture Model, Random Forest and K-Nearest Neighbors machine learning algorithms.
Part 1: April 27th
Part 2: May 11th
More details at: https://aiforgood.itu.int/events/workshop-satellite-data-analysis-and-machine-learning-classification-with-qgis-part-1/