Copernicus Foundation Models for a Thinking Earth

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.

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Use cases

Four Use Cases are being developed under ThinkingEarth, demonstrating socio-environmental impact and new business growth opportunities in the energy production, biodiversity monitoring and food security sectors.

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UC1 - Energy communities: Distributed solar energy production forecasting and demand management

Energy communities: Distributed solar energy production forecasting and demand management

UC2a - Biodiversity monitoring in urban environment

Biodiversity monitoring in urban environment

UC2b - Forest biomass monitoring: carbon sink assessment for the carbon credit industry

Forest biomass monitoring: carbon sink assessment for the carbon credit industry

UC3 - Causal inference for food insecurity analysis

Causal inference for food insecurity analysis

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Newsroom

Welcome to the ThinkingEarth newsroom. Here you will find the latest news, publications, and materials.

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ThinkingEarth’s Role in Transforming Greece’s Urban Energy Landscapes

30 October 2024

ThinkingEarth’s Role in Transforming Greece’s Urban Energy Landscapes

As Greece embraces a green energy transition, the potential of solar energy in its cities, especially via rooftop photovoltaic (PV) installations, has come to the forefront.

Protecting Water Resources with ThinkingEarth

27 September 2024

Protecting Water Resources with ThinkingEarth

Climate change is continuously on the rise and extreme weather events like heavy rainfall are becoming more frequent, leading to problems such as floods, landslides, and soil erosion.

ThinkingEarth: Exploring self-supervised learning techniques

ThinkingEarth: Exploring self-supervised learning techniques

Self-supervised learning on large-scale satellite data has gained interest for creating Earth observation (EO) foundation models

ThinkingEarth’s role in the Causal Impact of Humanitarian Aid on Food Security

ThinkingEarth’s role in the Causal Impact of Humanitarian Aid on Food Security

As climate change causes more droughts, vulnerable regions face serious food shortages, increasing the need for humanitarian aid. Communities that rely on rainfall for crop growth are especially at risk and prone to economic loss, risk of malnutrition and famine and subsequent devastating loss.

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Consortium

The ThinkingEarth consortium is a collaboration between research institutions, SMEs, industry partners, and end-user organisations.

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