Quick facts

• Who this is for: Graduate students, junior researchers, and EO enthusiasts
• Format: 3 days, in-person only, at Université Bretagne Sud, Tohannic Campus, Vannes
• Topics: Foundation Models for Earth Observation, MLOps, Responsible AI, and Generative Models
• Methods: Keynote lectures by leading scientists, hands-on coding sessions, and data-driven project with ESA Phi-lab
• Working Language: English
• Participation: Upon admission (free of charge)
• Deadline for Application: 3rd April, 2026

The OBELIX group is co-organizing (together with EJMD Copernicus Master in Digital Earth, Cluster SequoIA – PANORAMIX chair, and ESA Phi-lab) a Spring School on AI4EO, from April 8 to 10, 2026.

The registration is free of charge, but the number of participants is limited. Applications will be reviewed before selection. The application form is available here. We recommend the participants to register as soon as possible.

Academic recognition (3 ECTS) will be provided upon request and successful completion of activities.

The technical programme consists in lectures and hands-on on important topics such as Foundation Models for Earth Observation, MLOps, Responsible AI, and Generative Models. The school will also feature a short data-driven project with ESA Phi-lab, and include some social events.

Preliminary program

List of speakers

• Nicolas Audebert is a research scientist at IGN in the LASTIG lab. He obtained his PhD in 2018 from Université Bretagne-Sud on deep learning for remote sensing, and his habilitation from Université Gustave Eiffel in 2025. He specialises in representation learning for image generation and interpretation, with a focus on applications related to Earth observation. He co-organized multiple national workshops (explainable AI, generative AI, change detection), and is a co-organizer of the TerraBytes workshop.
• Pedram Ghamisi received the Ph.D. degree in electrical and computer engineering from the University of Iceland, Reykjavik, in 2015. He is currently Professor and Head of the Responsible AI Group at Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany, and Lancaster University, U.K. Previously, he was Senior Principal Investigator and Group Leader of AI4RS at the Institute of Advanced Research in Artificial Intelligence, Vienna, Austria, and co-founded VasogNosis, a U.S.-based startup with two branches.  His research focuses on deep learning for remote sensing, with particular emphasis on Open Science, AI for Good, Responsible AI, and AI Security, contributing significantly to the AI4EO era. He established the Responsible AI Working Group in IEEE GRSS and co-chairs GEO-AI4EO policy briefs to guide international AI practices in Earth Observation.  Prof. Ghamisi has received multiple international awards and led several major grants, including the IEEE GRSS Highest Impact Paper Award (2020, 2024), the IEEE GRSS Data Fusion Contest Winner (2017), the IEEE Mikio Takagi Prize (2013), the IEEE GRSS Best Reviewer Prize (2017), and grants such as the Helmholtz Foundation Models Initiatives (2024), the High Potential Program (HPP) Team Leadership (2018), and the Alexander von Humboldt Fellowship (2015). He serves as Associate Editor of the IEEE Transactions on Geoscience and Remote Sensing. For more information, please visit www.ai4rs.com.
• Pierre Adorni is currently a Ph.D. candidate at IRISA, Université Bretagne Sud, France. He received his engineering degree in Computer Science and his M.Sc. in Machine Learning & Optimization from UTC, Compiègne, France in 2024. His research interests include deep learning, computer vision, representation learning, and foundation models for Earth observation. His current work focuses on improving the generalization capabilities of machine learning models across diverse remote sensing datasets and tasks.
• Adam J. Stewart is a postdoctoral researcher at the Technical University of Munich in the Chair of Data Science in Earth Observation under the guidance of Prof. Xiaoxiang Zhu. His research interests lie at the intersection of machine learning and Earth science, especially remote sensing. He is the creator and lead developer of the popular TorchGeo library (https://github.com/torchgeo/torchgeo), a PyTorch domain library for working with geospatial data and satellite imagery. He received his B.S. from the Department of Earth and Atmospheric Sciences at Cornell University and his Ph.D. from the Department of Computer Science at the University of Illinois Urbana-Champaign.
• Stéphane May is a research engineer at CNES, the French space agency. He started his career in satellite telecommunications, focusing on payload design. He then transitioned to remote sensing, where he has built over 20 years of expertise in information extraction from satellite data. Through different projects, his technical activities span a wide range, including classification, data fusion, object detection, change detection, Digital Terrain Model extraction, anomaly detection, etc. Throughout his career, he has witnessed and contributed to the evolution from traditional machine learning to advanced deep learning techniques. At CNES, he plays a key role in multiple R&D projects dedicated to enhancing Artificial Intelligence training methodologies. With seven years of experience in edge AI, he significantly contributed to the AI onboard demonstration with the CO3D satellite, launched in 2025. Additionally, he is skilled in operating sensors and remote sensing platforms.
• Iris de Gélis obtained her M.Eng. in geomatics from the École Nationale des Sciences Géographiques (Marne-la-Vallée, France) in 2018, followed by an M.Sc. in fundamentals of physics for remote sensing from the Institut de Physique du Globe de Paris (IPGP) in 2019. She completed her Ph.D. at Université Bretagne Sud (Vannes, France) in 2023, where her research focused on deep learning methods for change detection in 3D point clouds. Her doctoral work was recognized with the Best PhD Thesis Award from the CNRS research group “Methods and Applications for Geomatics and Spatial Information,” as well as the Second PhD Prize (2023) from the French Association for Pattern Recognition (AFRIF). Since 2023, she has been working as a research engineer at Estellus and is also affiliated with the Paris Observatory (LIRA laboratory). Her current research interests focus on remote sensing using passive microwave data.
• Weikang Yu is a PhD researcher at the Helmholtz Institute Freiberg (HIF) for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany. He is also with the Chair of Data Science in Earth Observation, Technical University of Munich (TUM), Germany. He earned his M.Phil and B.E. degrees from the Chinese University of Hong Kong, Shenzhen, and Beihang University, respectively. His research focuses on developing cutting edge deep learning models for remote sensing (RS) applications, as parts of the Artificial Intelligence for Remote Sensing (AI4RS) studies. In particular, he is interested in the studies of change detection, responsible AI, and geospatial foundation models.
• Sébastien Lefèvre (M.Sc 1999, PhD 2002, Hab. 2009) is a Full Professor in Computer Science at Université Bretagne Sud (UBS) since 2010, promoted to exceptional class by the National Council of Universities in 2023. He is also Adjunct Professor at UiT – The Arctic University of Norway, Tromsø, Norway, since 2023, and Visiting Professor at ESA Phi-lab since 2025. He is involved in numerous activities related to Artificial Intelligence for Earth and Environment Observation, including: founder of the OBELIX team (www.irisa.fr/obelix) within Institute for Research in Computer Science and Random Systems (IRISA), chair of the GeoData Science track of the EMJM Copernicus Master in Digital Earth (www.master-cde.eu), co-founder of the ECML-PKDD MACLEAN workshop series on Machine Learning for Earth Observation, chair of the AI4EO 2025 symposium, coordinator of the UBS-JRC Collaborative Doctoral Program on AI4EO, and holder of the PANORAMIX experienced chair within the SequoIA AI cluster (2025-2029).