IEEE GRSS and IADF School on Computer Vision for Earth Observation

  • Course Program

Remote sensing techniques are utilized for a better understanding of the Earth and have impacted applications in weather forecasting, tracking biodiversity, land management, post-disaster relief, rescue management, and policymaking, to name a few. The amount of data acquired from sensors onboard Earth Observation (EO) satellites is increasing. For example, it is expected that Sentinel satellites will produce ten terabytes of EO data once fully operational. The availability of data from multiple sensors and satellites provides an opportunity to develop innovative methodologies for remote sensing applications.

As the IEEE GRSS Image Analysis and Data Fusion Technical Committee (IADF-TC), after the success of the first school on Computer Vision for Earth Observation (CV4EO), we are pleased to announce its second edition open to everybody who has a strong motivation and interest in the topics addressed by it. It was held in presence at the University of Sannio, in Benevento (Italy) from September 13 to 15, 2023. This school focusses on applying CV methods to address challenges in remote sensing. It contains a series of 5 lectures on the existing methods utilized for analyzing satellite images, along with the challenges encountered.


Devis Tuia

Devis completed his PhD at University of Lausanne, Switzerland, where he studied kernel methods for hyperspectral satellite data. He then traveled the world as a postdoc, first at University of València, then at CU Boulder and finally back to EPFL. In 2014, he became assistant professor at University of Zurich, and in 2017 he moved to Wageningen University in the Netherlands, where he was chair of the Geo-Information Science and Remote Sensing Laboratory. Since September 2020, he is back to EPFL, where he leads the Environmental Computational Science and Earth Observation laboratory (ECEO) in Sion. There, he studies the Earth from above with machine learning and computer vision.

Florence Tupin

Florence Tupin(Senior Member, IEEE) received the engineering degree and the Ph.D. degree in signal and image processing from Ecole Nationale Superieure des Telecommunications (ENST), Paris, France, in 1994 and 1997, respectively, and the Habilitation a Diriger des Recherches degree from the University of Rennes, Rennes, France, in 2007. From 1997 to 1998, she was with SAGEM, Paris, France, where she worked on fingerprint recognition. She is currently a Professor of image and signal processing with LTCI, Telecom Paris, Paris, France. From 2014 to 2020, she was the Head of the Image, Modeling, Analysis, GEometry,and Synthesis Team of LTCI, and since 2020 she has been
deputy director of LTCI. She has coauthored more than 200 papers. Her research interests include image processing and analysis, especially for remote sensing and synthetic aperture radar imaging applications, and earth observation. Pr. Tupin has been a member of several international and national technical conference committees since 2003. She was the Chair of the Urban Remote Sensing Joint Event held in Paris in 2007. She was an Associate Editor for the IEEE Transactions on Geoscience and Remote Sensing
from 2007 to 2016. She was the recipient of several awards, among them the IEEE GRSS Transactions Prize Paper Award in 2016 and the IEEE GRSS Symposium Prize Paper Award in 2022 for works on speckle filtering.

Michael Mommert

Michael Mommert graduated in physics from the University of Heidelberg, Germany, in 2009, and received a Ph.D. degree in Earth sciences from Free University Berlin, Germany, in 2013. Currently, he is Assistant Professor for computer vision at the School of Computer Science, University of St. Gallen, Switzerland. His research focuses on the intersection of computer vision and remote sensing with an emphasis on data-efficient deep learning methods. Before joining the University of St. Gallen in 2020, he conducted research in solar system astronomy at Lowell Observatory and Northern Arizona University, Flagstaff, USA.

Paolo Gamba

Paolo Gamba received the Laurea degree in electronic engineering (cum laude) and the Ph.D. degree in electronic engineering from the University of Pavia, Pavia, Italy, in 1989 and 1993, respectively. He is a Professor with the University of Pavia, where he leads the Telecommunications and Remote Sensing Laboratory. He has been invited to give keynote lectures and tutorials on several occasions about urban remote sensing, data fusion, EO data for physical exposure, and risk management. He has authored or coauthored more than 140 articles in international peer-reviewed journals and presented nearly 300 research works in workshops and conferences.

Dr. Gamba was the Chair of the Data Fusion Committee of the IEEE Geoscience and Remote Sensing Society (GRSS) from October 2005 to May 2009. He has been elected in the GRSS AdCom, since 2014, and he was the GRSS President (2019-2020). He has been the Organizer and the Technical Chair of the Biennial GRSS/ISPRS Joint Workshops on Remote Sensing and Data Fusion over Urban Areas from 2001 to 2015. He also served as the Technical Co-Chair for the 2010 and 2015 IGARSS Conferences, in Honolulu (Hawaii) and Milan (Italy), respectively. He served as an Editor-in-Chief for the IEEE Geoscience and Remote Sensing Letters from 2009 to 2013 and he is the Editor-in-Chief for the IEEE Geoscience and Remote Sensing Magazine. He is Fellow of IEEE, IAPR, and AAIA.

Ribana Roscher

Ribana Roscher is a Professor of Data Science for Crop Systems at the University of Bonn, Germany, and heads the same-titled group at the Institute of Bio- and Geosciences at Research Center Jülich, Germany. Until 2022, she was a Junior Professor of Remote Sensing with the University of Bonn. Before she was a Postdoctoral Researcher with the University of Bonn, the Julius-Kühn Institute, Siebeldingen, Germany, Freie Universität Berlin, Germany, and the Humboldt Innovation, Berlin, Germany. In 2015, she was a Visiting Researcher with the Fields Institute, Toronto, Canada, and in 2018 she was a Visiting Researcher with UCLA Institute for Pure & Applied Mathematics, Los Angeles, USA. Her research focus is pattern recognition and machine learning specifically for applications in agricultural and environmental sciences. She currently chairs the IAPR Technical
Committee 7 ‘Remote Sensing and Mapping’ and the ISPRS Working Group ‘Machine Learning for Geospatial Data’.

Course Length: 15 hours

Publication Year: 2023

IEEE GRSS and IADF School on Computer Vision for Earth Observation
  • Course Provider: Geoscience and Remote Sensing Society
  • Course Number: GRSS012
  • Credits: 1.5 CEU/ 15 PDH