Xiao Xiang Zhu
Xiao Xiang Zhu is an IEEE Fellow, and the Chair Professor for data science in Earth observation with the Technical University of Munich (TUM), Munich, Germany, where she is the Director of the Munich Data Science Institute. She is currently a Visiting AI Professor with the European Space Agency’s φ-lab, in Frascati (Rome), Italy. Since 2022, she leads the National Center of Excellence ML4Earth (ml4earth.de), funded by the German Space Agency (DLR). Her research interests include Earth observation and remote sensing, signal processing, machine learning, and data science, with applications to global urbanization, the UN Sustainable Development Goals, and climate change. More information is available at https://www.asg.ed.tum.de/en/
Jocelyn Channusot
Jocelyn Chanussot (M’04–SM’04–F’12) received the M.Sc. degree in electrical engineering from the Grenoble Institute of Technology (Grenoble INP), Grenoble, France, in 1995, and the Ph.D. degree from the Université de Savoie, Annecy, France, in 1998. From 1999 to 2023, he has been with Grenoble INP, where he was a Professor of signal and image processing. He is currently a Research Director with INRIA, Grenoble. His research interests include image analysis, hyperspectral remote sensing, data fusion, machine learning and artificial intelligence. He has been a visiting scholar at Stanford University (USA), KTH (Sweden) and NUS (Singapore). Since 2013, he is an Adjunct Professor of the University of Iceland. In 2015-2017, he was a visiting professor at the University of California, Los Angeles (UCLA). He holds the AXA chair in remote sensing and is an Adjunct professor at the Chinese Academy of Sciences, Aerospace Information research Institute, Beijing. Dr. Chanussot is the founding President of IEEE Geoscience and Remote Sensing French chapter (2007-2010) which received the 2010 IEEE GRS-S Chapter Excellence Award. He has received multiple outstanding paper awards. He was the Vice-President of the IEEE Geoscience and Remote Sensing Society, in charge of meetings and symposia (2017-2019). He was the General Chair of the first IEEE GRSS Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote sensing (WHISPERS). He was the Chair (2009-2011) and Cochair of the GRS Data Fusion Technical Committee (2005-2008). He was a member of the Machine Learning for Signal Processing Technical Committee of the IEEE Signal Processing Society (2006-2008) and the Program Chair of the IEEE International Workshop on Machine Learning for Signal Processing (2009). He is an Associate Editor for the IEEE Transactions on Geoscience and Remote Sensing, the IEEE Transactions on Image Processing and the Proceedings of the IEEE. He was the Editor-in-Chief of the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2011-2015). In 2014 he served as a Guest Editor for the IEEE Signal Processing Magazine. He is a Fellow of the IEEE, an ELLIS Fellow, a Fellow of the Asia-Pacific Artificial Intelligence Association, a member of the Institut Universitaire de France (2012-2017) and a Highly Cited Researcher (Clarivate Analytics/Thomson Reuters, since 2018).
Begüm Demir
Begüm Demir is currently a Full Professor and the founder head of the Remote Sensing Image Analysis (RSiM) group at the Faculty of Electrical Engineering and Computer Science, TU Berlin. She is the co-director of the Berlin Institute for the Foundations of Learning and Data (BIFOLD), where she leads the Big Data Analytics for Earth Observation research group. Her research activities lie at the intersection of machine learning and data management for Earth observation. Prof. Demir’s scientific excellence has been widely acknowledged through numerous honors. In 2018, she received the Early Career Award from IEEE Geoscience and Remote Sensing Society for her research on machine learning techniques for information retrieval in remote sensing. She was awarded two prestigious grants from the European Research Council (ERC): following a 2018 Starting Grant for her foundational project “BigEarth”, she has been recently awarded an ERC Proof of Concept Grant for “Agent BigEarth”. This latest project aims to develop an AI agent to enhance environmental intelligence through direct interaction with Copernicus Earth observation data. She is a Senior Member of the IEEE and a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS).
Ronny Hänsch
Ronny Hänsch received the Diploma degree in computer science and the Ph.D. degree from the Technische Universität Berlin, Berlin, Germany, in 2007 and 2014, respectively. He is currently a Senior Scientist with the Microwave and Radar Institute, German Aerospace Center (DLR), where he leads the Machine Learning Team in the Signal Processing Group of the SAR Technology Department. Dr. Hänsch has held several leadership roles, including Chair of the IEEE GRSS Image Analysis and Data Fusion (IADF) Technical Committee, Chair of the GRSS German Chapter, GRSS Membership Chair, and GRSS Representative within SpaceNet, as well as Co-Chair of the ISPRS Working Group on Image Orientation and Sensor Fusion. He co- organizes the CVPR Workshop EarthVision and the IGARSS Tutorial on Machine Learning in Remote Sensing and was Technical Lead of the SpaceNet 8 and 9 Challenges. He is Editor-in Chief of the IEEE Geoscience and Remote Sensing Letters and Associate Editor of the ISPRS Journal of Photogrammetry and Remote Sensing and the IEEE Open Journal for Computer Science. His research interests include machine learning for SAR and optical remote sensing, with emphasis on semantic segmentation, multi-modal data, and ensemble learning for large-scale Earth observation.
Clément Mallet
Clément Mallet is currently a Senior Researcher in geospatial computer vision. His research interests include point cloud and optical image processing, land-cover classification, and more generally multimodal remote sensing. He is the former director of the LASTIG with the French National Institute for Geographic Information and Forestry (IGN) and Université Gustave Eiffel (France). He is the Editor-in-Chief of the ISPRS Journal of Photogrammetry and Remote Sensing since 2021 and co-chair of the biennal JURSE conference related to urban remote sensing. He is Deputy Director of Théia@Data Terra, the French consortium related to Earth Observation for land-surfaces. Previously, he served as the Editor-in-Chief for the French Journal of Photogrammetry and Remote Sensing between 2011 and 2015 and was Program chair of various events such as the ISPRS Congress 2020 and 2021 and the ISPRS Geospatial Week 2015.
Elliot Vincent
Elliot Vincent recently completed his Ph.D. in Computer Science at École nationale des ponts et chaussées and Inria in Paris, France. He is now an AI Coordinator and researcher at the French National Institute of Geographic and Forest Information (IGN), affiliated with the LASTIG laboratory. His research interests include machine learning and computer vision for remote sensing, with a focus on change detection and deep learning applied to 2D and 3D geospatial data. He has published at leading computer vision venues, including CVPR, ICCV and ICDAR, contributing to topics such as unsupervised scene decomposition, 3D aerial scan parsing, and large-scale visual geolocation, His recent contributions span satellite image time series analysis, semantic change detection, and the monitoring of archaeological sites from satellite imagery.
Sining Chen
Sining Chen received the Bachelor's degree in marine science at Xiamen University, Xiamen, China, in 2018, and the Master's degree in Earth-oriented Space Science and Technology (ESPACE) at Technical University of Munich (TUM), Munich, Germany in 2020. He is pursuing a Ph.D. degree at the Chair of Data Science in Earth Observation at Technical University of Munich (TUM) since September 2021. He was a DLR/DAAD Doctoral Research Fellow at the Remote Sensing Technology Institute, German Aerospace Center (DLR), Wessling, Germany, from September 2021 to August 2023. His research interests include deep learning, monocular height estimation, and 3D building reconstruction.