The Deep Earth System Data Lab (DeepESDL, https://earthsystemdatalab.net) provides an AI-ready, collaborative environment enabling researchers to understand the complex dynamics of the Earth System using numerous datasets and multi-variate, empirical approaches. The solution builds on work done in previous projects funded by the European Space Agency (CAB-LAB and ESDL), which esta-blished the technical foundations and created measurable value for the scientific community (e.g., Mahecha et al. (2020) or Flach et al. (2018)). DeepESDL relies heavily on the well-established open-source technology stacks for data science in Python, thus ensuring usability and compatibility.
Within DeepESDL we realize the implementation and execution of Machine Learning workflows on Analysis Ready Data Cubes (ARDCs) in a reproducible and FAIR way, allowing sharing and versioning of all ML artifacts like code, data, models, execution parameters, metrics, and results as well as tracking each step in the ML workflows (supported by integration with open-source tools like TensorBoard or mlflow) for an experiment so that others can reproduce them and contribute.
The ML tools for ARDCs involve efficient and reasonable data loading and resampling strategies, which consider the special characteristics of geo-spatial data (e.g., data gaps or their uneven distribution across the sphere).
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