ThinkingEarth - Copernicus Foundation Models for a Thinking Earth
Background & Objectives
At ThinkingEarth, we view the Earth as a complex unified and interconnected system. To harness the power of Artificial Intelligence (AI), we use cutting-edge techniques, including deep learning, causality, eXplainable AI, and physics-aware Machine Learning. We leverage the predictive abilities of Self-Supervised Learning and Graph Neural Networks to develop task-agnostic Copernicus Foundation Models and a Graph representation model of the Earth.
We demonstrate the potential of these assets through small-scale downstream Spotlight Applications, as well as large-scale use cases that integrate distributed industrial and user non-EO datasets. These use cases address ambitious problems with high socio-environmental impact and new business growth opportunities, such as accelerating Europe's clean energy transition and independence from volatile fossil fuels, understanding Earth's processes by modeling causal Earth system teleconnections, and assessing and modeling the impact of current and future Climate emergency in biodiversity and food security.