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¹ Université Bretagne Sud, IRISA, Vannes, France
² Wageningen University, Wageningen, the Netherlands
³ Belgian Science Policy Office, Brussels, Belgium
Charlotte Pelletier is an associate professor in computer science at Univ. Bretagne Sud, Vannes, France. She conducts her research in the Obelix team at the Institute for Research in IT and Random Systems (IRISA), Vannes, France. Her research focuses on machine learning, in particular time series analysis with applications in remote sensing. She works on classification and regression tasks, unsupervised domain, or super-resolution. She is the coleader of the GeoData Science track within the Erasmus Mundus Joint Master named Copernicus Master in Digital Earth. She was a research fellow at the Faculty of Information Technology, Monash University, Melbourne, Australia (2018-2019). She completed her PhD in December 2017, funded by the French Space Agency (CNES) and the French Mapping Agency (IGN) at CESBIO laboratory in Toulouse, France. Her Ph.D work aimed at improving the classification of new high resolution satellite image time series, such as the one produced by Sentinel-2 satellites. This work has been awarded best PhD in Science by the Académie des Sciences of Toulouse in 2018.
Marc Rußwurm is Assistant Professor of Machine Learning and Remote Sensing at Wageningen University. His background is in Geodesy and Geoinformation, and he obtained a Ph.D. in Remote Sensing Technology at TU Munich. During his Ph.D., he could visit the European Space Agency and the University of Oxford as a participant in the Frontier Development Lab in 2018, the Obelix Laboratory in Vannes, and the Lobell Lab in Stanford. As a postdoctoral researcher, he joined the Environmental Computational Science and Earth Observation Laboratory at EPFL, Switzerland. His research interests are developing modern machine learning methods for real-world remote sensing problems, such as classifying vegetation from satellite time series and detecting marine debris in the oceans. He is interested in domain shifts and transfer learning problems naturally arising from geographic data.
Dainius Masiliunas is Lecturer and PhD candidate in Remote Sensing, with skills in ecology and interest in information technology. In his doctoral research, he continues to develop the popular BFAST, Breaks For Additive Season and Trend, software that is used broadly for unsupervised breakpoint detection in time series. His focus is on detecting and analyzing land cover change using satellite sensors.
Jan Verbesselt is a program manager at the Belgian Science Policy Office, Brussels and follows the Earth Observation programs at ESA and Copernicus EU. He has developed the open-source toolkit, BFAST, which provides functionality to detect, monitor, and characterize change within satellite image time series (http://bfast.r-forge.r-project.org/).