Workshop: Satellite Data Analysis and Machine Learning Classification with QGIS – Part 2

In collaboration with

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.

Besides the two plugins, some core QGIS functionalities and are included in the workshop for clipping satellite imagery and creating vector file of training data. Lastly, outcomes of the machine learning algorithm are compared with the global map of human settlements – GHS-BUILT (Sentinel-1) produced by Joint Research Center (JRC) of European Commission to assess their performance. Before being used for assessing algorithms’ performances, GHS-BUILT (Sentinel-1) is adapted to coordinate reference system, resolution, and classes of classification outcomes. Adaptation of GHS-BUILT (Sentinel-1) involves many isolated operations (reprojection, tile merging, resampling, and reclassification). For this reason, the QGIS Graphical Modeler is introduced in the exercise because it allows automation of chain of operations. Besides the adaptation of GHS-BUILT (Sentinel-1), the computation of error matrix and accuracy indexes for each classification outcome are integrated with the Graphical Modeler too.

Please see the two one-pager guidelines for the software requirements and the datasets for the exercises and download relevant software and datasets before the workshop.

The workshop has two parts:

  • Part 1: 27 April 2021, 14:00 – 16:00 CEST
  • Part 2: 11 May 2021, 14:00 – 16:00 CEST

Speakers, Panelists and Moderators

    Professor of Geographic Information Systems and Digital Mapping
    Politecnico di Milano

    Degree with honors in Physics, PhD in Geodesy and Cartography. She is Professor of GIS at the Politecnico di Milano (PoliMI) and member of the School of Doctoral Studies in Data Science at “Roma La Sapienza”. From 1997 to 2010 she was the Director of the “Geomatics Lab” of PoliMI. From 2011 to 2016 she was the Vice Rector of PoliMI for the Como Campus.

    She is the chair of ISPRS WG IV/4 “Collaborative crowdsourced cloud mapping (C3M)”; member of ESA ACEO (Advisory Committee of Earth Observation); co-chair of the United Nations Open GIS Initiative, Deputy-Chair of the UN-GGIM (Global Geospatial Information Management) Academic Network, mentor of the PoliMI Chapter of YouthMappers (PoliMappers). She is author of 108 scientific indexed publications and Guest Editor of 11 Special Issues.

    Her research activity is in the field of geomatics. Her interests have been various, starting from geodesy, radar-altimetry and moving later to GIS, webGIS, geospatial web platform, VGI (Volunteer Geographic Information), Citizen Science and Big Geo Data. She is participating and leading research on these topics within the frameworks of both national and international projects and scientific networks. One of her main interest is in Open Source GIS, where she is playing a worldwide leading role.

    Research Fellow
    Politecnico di Milano
    Eng. Gorica Bratic obtained her BSc degree in Environmental Engineering at University of Novi Sad, Faculty of Technical Sciences in 2015 and MSC degree in Environmental and Geomatics Engineering at Politecnico di Milano in 2018. In June 2018 she joined the GEOlab team of Politecnico di Milano as a temporary research fellow and in November 2018 she started her PhD in the Department of Civil and Environmental Engineering. Her main field of interest is inter-comparison and validation of global high-resolution land cover maps. Her research relies on Free and Open Source Software technologies.


11 May 2021


CEST, Geneva
14:00 - 16:00