AI Strategy for Earth System Data
AI for the analysis of Earth Observation data
Artificial intelligence (AI) methods currently experience rapid development and increasing use in the context of environmental data. However, this often happens in isolated solutions. Environmental and earth system sciences have yet to establish the systemic use of modern AI methods. In particular, discrepancy exists 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 KI:STE project strives to facilitate and evaluate the use of AI for remote sensing Earth Observation data for a range of applications. The fields studied in the project range from air quality to clouds and radiation, to snow and ice propagating, as well as water that drives vegetation, then closing the loop with air quality again. A core focus is not only to adopt and apply AI concepts to these areas, but also to train several PhD students and build an e-learning platform. This will ease and facilitate access to the algorithms and tools developed for a wider audience – from scientists to practitioners.
52°North develops the Spatial Research Data Infrastructure (SRDI) that will supply the AI processing platform with data. A requirement analysis provides the basis for defining and developing interfaces for data acquisition and provision. The platform must react flexibly to the requirements of the AI algorithm requesting data in order to be able to provide them in a format optimized for the required processing. We will work on the SRDI in close collaboration with the Ambrosys GmbH.
The project officially started in November 2020. As a result, our team and Ambrosys have initiated the requirements analysis for the KI:STE data and machine learning platform. We are already evaluating first concepts and options according to their suitability.