Geoinformation derived from Earth observation satellite data is indispensable for tackling grand societal challenges, such as urbanization, climate change, and the UN’s SDGs. Furthermore, Earth observation has irreversibly arrived in the Big Data era, e.g. with ESA’s Sentinel satellites and with the blooming of NewSpace companies. This requires not only new technological approaches to manage and process large amounts of data, but also new analysis methods. Here, methods of data science and artificial intelligence, such as machine learning, become indispensable. This talk showcases how innovative machine learning methods and big data analytics solutions can significantly improve the retrieval of large-scale geo-information from Earth observation data, and consequently lead to breakthroughs in geoscientific and environmental research. In particular, by the fusion of petabytes of EO data from the satellite to social media, fermented with tailored and sophisticated data science algorithms, it is now possible to tackle unprecedented, large-scale, influential challenges, such as the mapping of urbanization on a global scale, with a particular focus on the developing world.
Xiaoxiang Zhu (Technical University of Munich)
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