ML4Earth

Funded by BMWK

Project Leader
Prof. Dr. Xiaoxiang Zhu

Cooperation Partners
University of Bonn (Prof. Dr. Jürgen Kusche), University of Leipzig (Remote Sensing Centre for Earth System Research), University of Bristol (Use Case Sea-Level Rise), Alfred Wegener Institut (Use Case Permafrost), DLR (Earth Observation Center)

Runtime
2022 – 2024

 

The national excellence center “Machine Learning for Earth Observation” (ML4Earth) will conduct own research at the highest international level by tackling fundamental methodical challenges in AI4EO and their application to the European mission of a Digital Twin Earth. ML research directions will include physics-aware machine learning, reasoning, uncertainty estimation, Explainable AI, Sparse Labels and Transferability, as well as Deep Learning for Complex Data Structures. By investing significant effort in advancing the community’s knowledge in these fields, we create direct impact in various application fields and are shaping our future globally. We will be able to assess the uncertainties of future sea level rise, make it more transparent and explainable how AI algorithms capture the increasing threat to global forests due to the increasing temperatures, quantify the inherent phenomenon of rapid permafrost thawing, use physical hydrological models paired with AI data science to predict Europe’s future ability to store water in cities given the increasing threat of extreme weather, map out physical and chemical soil parameters at large scale based on sparse knowledge on the soil parameters of quite localized areas, or apply deep learning to comprehend the complexity behind climate tipping points.