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Earth Data Science: Integrating datasets and machine learning techniques to understand geospatial processes

Earth science and engineering is at the core of several pressing societal questions. Climate change impacts the dynamics of our planet, from the atmosphere to the oceans and continents. The need to move away from fossil fuels means we will need to rely more on so called ‘Geo-energies’, such as hydrothermal heat, geological reservoir of hydrogen, and carbon capture and storage in the subsurface. And humanity’s constant pull towards exploration of the planets in our solar system also mean that an understanding of planetary processes is needed.

Earth Data Science is characterized by challenging datasets: the data encompasses a large range of temporal and spatial scales, and it typically is large (‘Big Data’) and spatially sparse. Machine learning and deep learning approaches show great promises in this field, but optimal application of these approaches in Earth Data Science is not straightforward: this is what our proposal aims to address. Our research initiative leverages existing research and advances it by promoting integration across fields and across departments to answer relevant societal problems and builds upon our strong foundation of teaching master level courses in data science and machine learning.

Led by Dr Cédric M. John.

Our research