The new national center of excellence ML4Earth (Machine Learning for Earth observation) brings together “hardcore” AI experts, remote sensing scientists and Earth scientists to tackle fundamental Earth observation domain-specific machine learning challenges. We demonstrate the impact of ML4Earth with a wide range of use cases related to the realization of a Digital Twin of Earth. It is also our mission to shape a strong Earth observation Data Science Community.
Our research follows a high-risk / high-gain approach. We work highly interdisciplinary and make best use of Data Science (Artificial intelligence / machine learning) for Earth observation data, having in mind grand challenges of society via our covered application fields, ultimately contributing to the Digital Twin of Earth.
Our unique selling point is our profound expertise in AI and data science in Earth observation. We pioneer the development of novel AI methods for big Earth observation data and form one of the leading research centers worldwide in that field. The research community can benefit from our expertise by attending our workshops or using benchmark datasets that we provide.
The main research focus of ML4Earth is to bring together novel AI methods for Earth observation purposes and Earth and Climate Sciences. We are covering six major research branches, each linked to a specific application field.
Physics-aware machine learning
With physics-aware machine learning, we use physical laws and physics-domain-specific knowledge to improve the performance and validity of our data-driven machine learning models.
Publications of our research team will be listed here
Products & Publications
Products and Publications
An important goal of ML4Earth is to build and maintain an international community within the AI4EO domain. We are pursuing this goal together with the Space Agency of the German Aerospace Center. We are doing so by providing benchmark datasets as a service to the community, while offering the community opportunities for training and networking.