Reinventing HPC

Conference Keynote 2021

Prof. Dr. Xiaoxiang Zhu

Professor, Data Science in Earth Observation,
TU Munich & German Aerospace Center


(Monday, June 28)

Geoinformation derived from Earth observation satellite data is indispensable for many scientific, governmental and planning tasks. Geosciences, atmospheric sciences, cartography, resource management, civil security, disaster relief, as well as planning and decision support are just a few examples of how this data can be used. With the entrance of earth observation into the big data era, we see applications being undertaken by the European Space Agency’s Sentinel satellites, along with use cases being pioneered by emerging NewSpace companies. These require not only new technological approaches to manage and process large amounts of data, but also new analysis techniques. Here, data science methods and artificial intelligence (AI), such as machine learning, are indispensable.
In her conference keynote, Professor Xiaoxiang Zhu will discuss how breakthroughs in geoscientific and environmental research can be achieved using these new methods. In particular, she will explain how explorative signal processing and machine learning algorithms, like compressive sensing and deep learning, can significantly improve information retrieval from remote sensing data. By applying cutting-edge data science algorithms to petabytes of Earth Observation data derived from everything from satellites to social media, it is now possible to tackle large-scale challenges, such as the mapping of global urbanization, with unprecedented sophistication.



Xiaoxiang Zhu is the Professor for Data Science in Earth Observation at the Technical University of Munich where she is the co-director of the Munich Data Science Institute, the head of the department “Earth Observation Data Science” at the German Aerospace Center, the Director of the international AI future Lab “AI4EO”, the co-spokeswoman of the Munich Data Science Research School (MUDS), and the head of the Helmholtz Artificial Intelligence – Research Field "Aeronautics, Space and Transport". The research of Xiaoxiang focuses on signal processing and data science in Earth observation. She develops innovative machine learning methods and big data analytics solutions to extract geo-information from big EO data. Her team aims at tackling societal grand challenges, e.g. Global Urbanization, UN’s SDGs and Climate Change, thus, works on solutions that can scale up for global applications. Xiaoxiang is the recipient of the Heinz Maier-Leibnitz-Preis of the German Research Foundation (DFG), Innovators under 35 of Technology Review Germany, ERC Starting Grant, PRACE Ada Lovelace Award for HPC, Helmholtz Excellence Professorship and, most recently, the Leopoldina Early Career Award of the German National Academy of Sciences for "her outstanding achievements in satellite-based Earth observation for the assessment of global urbanization and natural hazards". Xiaoxiang is a Fellow of IEEE.


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