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Tackling Food Insecurity with ThinkingEarth
News Item
13 June 2025

Tackling Food Insecurity with ThinkingEarth

Food insecurity is becoming one of the most urgent global challenges of our time. In 2023, alone, more than 345 million people were projected to be food insecure—more than double the number just three years earlier. The causes are complex: poverty, conflict, economic shocks, and especially climate change, which disrupts food systems through droughts, floods, and extreme weather events that reduce crop yields and damage infrastructure.
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A Smart Grid Future with ThinkingEarth
News Item
29 May 2025

A Smart Grid Future with ThinkingEarth

In April 2025, a widespread blackout hit Spain and Portugal, disrupting essential services, public transportation, and daily life. The incident highlighted a growing vulnerability in modern energy systems: the challenge of managing high shares of renewable energy—especially solar—without sufficient real-time oversight and coordination.
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Neuroplasticity-Inspired Foundation Models for Earth Observation
News Item
30 April 2025

Neuroplasticity-Inspired Foundation Models for Earth Observation

As the volume, diversity, and complexity of satellite Earth observation (EO) data continue to surge, the need for intelligent models that can seamlessly integrate multimodal information has never been more pressing. At ThinkingEarth, our mission has always centered on unlocking the full potential of EO data, through adaptive, scalable, and generalisable AI solutions.
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How ThinkingEarth Leverages Large-Scale Datasets
News Item
27 March 2025

How ThinkingEarth Leverages Large-Scale Datasets

At ThinkingEarth, we harness the power of Self-Supervised Learning (SSL) and Graph Neural Networks (GNNs) to construct task-agnostic Copernicus Foundation Models and a graph-based representation of the Earth. By integrating diverse Earth Observation (EO) datasets, we aim to improve environmental monitoring, climate action, and sustainable development.
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ThinkingEarth and Copernicus Foundation Models: Advancing Earth Observation
News Item
28 February 2025

ThinkingEarth and Copernicus Foundation Models: Advancing Earth Observation

At ThinkingEarth, we are developing advanced Artificial Intelligence (AI) techniques to better understand our planet as a complex system. Using Deep Learning (DL), eXplainable AI (xAI), and physics-aware Machine Learning (ML), we enhance Earth observation (EO).
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Enhancing Land Cover Mapping with ThinkingEarth’s Self-Learning Techniques
News Item
30 January 2025

Enhancing Land Cover Mapping with ThinkingEarth’s Self-Learning Techniques

Mapping land cover is important for tracking environmental changes, managing natural resources, and making decisions about cities, farms, and climate. Traditional methods need a lot of manual work and labelled data, which can be slow and costly. ThinkingEarth's self-supervised learning (SSL) technique can offer a better way to map land cover accurately and efficiently, using large amounts of unlabelled satellite data.
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Graph Neural Networks and ThinkingEarth
News Item
12 December 2024

Graph Neural Networks and ThinkingEarth

Graphs are a powerful way to represent complex relationships between different entities, making them essential for understanding intricate systems.
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ThinkingEarth and Flood Mapping: Enhancing the Future of Disaster Response
News Item
29 November 2024

ThinkingEarth and Flood Mapping: Enhancing the Future of Disaster Response

Flood mapping has long relied on satellite-based datasets, which provide critical insights into disaster impact. However, these datasets often face challenges, such as limited temporal coverage, inconsistent annotations, and the absence of pre-event imagery.
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ThinkingEarth’s Role in Transforming Greece’s Urban Energy Landscapes
News Item
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.
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Scientific Publications

Since the launch of ThinkingEarth, our partners have been actively contributing to scientific progress in AI for Earth Observation. A number of peer-reviewed articles and conference papers have already been published, showcasing the project’s dedication to advancing research and innovation.

These publications help raise awareness of ThinkingEarth’s results within the scientific community and support wider adoption of our methodologies and models.

Explore the list of publications from the project below

Multi-Layer Cloud Motion Vector Forecasting for Solar Energy Applications

Multi-Label Guided Supervised Contrastive Learning for Earth Observation Pretraining

Multilabel-Guided Soft Contrastive Learning for Efficient Earth Observation Pretraining

MUDDAT: A Sentinel-2 Image-Based Muddy Water Benchmark Dataset for Environmental Monitoring

Ray-Tracing modeling for urban photovoltaic energy planning and management

Assessing the causal impact of humanitarian aid on food security

Sunrise Strategies: Maximizing Solar Energy Yields AMIDST Environmental Variabilities in Egypt

Conditions for Producing Solar Photovoltaic Energy in European and North African Cities

Hybrid Deep Learning for Oil Spill Mapping: Leveraging Sentinel-2 and Foundation Models

Comparative Analysis of Learning-Based Approaches for Change Detection in Satellite Images

Kuro Siwo: 33 billion m 2 under the water. A global multi-temporal satellite dataset for rapid flood mapping

FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring

Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment

On the Generalization of Representation Uncertainty in Earth Observation

On the Foundations of Earth and Climate Foundation Models

Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation

Self-Supervised Pretraining With Monocular Height Estimation for Semantic Segmentation

Photovoltaics Energy Potential in the Largest Greek Cities: Atmospheric and Urban Fabric Effects, Climatic Trends Influences and Socio-Economic Benefits
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