Artificial intelligence (AI) methods are currently experiencing rapid development and are also being used more and more frequently in the context of environmental data. However, this is often done in the context of isolated solutions. The systematic use of modern AI methods is not yet established in environmental and earth system sciences. In particular, there is often a discrepancy between the requirements of a solid and technically sound environmental data analysis and the applicability of modern AI methods such as Deep Learning for researchers.
The project KI:STE (AI strategy for earth system data, in German: KI-Strategie für Erdsystemdaten) closes this discrepancy with a sophisticated strategy that combines the development of diverse AI applications on different socially relevant aspects of environmental and earth system research with a strong training and network concept. It creates the technical prerequisites to make high-performance AI applications on environmental data portable for future users and to establish environmental AI as a key technology.
KI:STE is funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) for 3 years. The project will start on 1 November 2020 giving the opportunity to develop an AI-platform and compose an earth science database in collaboration with the University of Cologne, the University of Bonn, RWTH Aachen, Ambrosys GmbH, 52°North GmbH and the Institute of Bio- and Geosciences. The Jülich Supercomputing Centre is coordinator of the project and will work on developing a scalable machine learning workflow towards the AI-platform.
Courtesy to ESA for the nice satellite picture at the top.