On the foundations of Earth foundation models

About this episode

The episode discusses the shortcomings of current Earth AI models and the need for improved foundation models to address climate challenges.

Citation : Zhu, X. X., Xiong, Z., Wang, Y., Stewart, A. J., Heidler, K., Wang, Y., Yuan, Z., Dujardin, T., Xu, Q., & Shi, Y. (2026). On the foundations of Earth foundation models. Communications Earth & Environment , 7, 103. https://doi.org/10.1038/s43247-025-03127-x Main Takeaways: Current Earth AI Models Are Missing the Point : Researchers have identified eleven features that an ideal Earth foundation model must have — including geolocation awareness, multi-sensor integration, physical consistency, and carbon minimization — yet no existing model comes close to checking all eleven boxes. Most models focus on only one or two features, leaving a major gap between what we have and what we actually need to tackle real-world climate and environmental challenges. The Data Situation Is More Lopsided Than You'd Think : There are now over 1,000 active remote sensing satellites generating nearly 100 petabytes of open satellite data — but labeled datasets used to train AI models account for less than 0.1% of that archive. This massive imbalance is precisely why self-supervised foundation models, which can learn from unlabeled data, are so critical for Earth science going forward…

People in this episode

Host: Amirpasha

Topics covered

• Earth foundation models
• AI in Earth science
• climate challenges
• remote sensing
• self-supervised learning
• weather forecasting

Keywords

• Earth AI models
• foundation models
• remote sensing satellites
• self-supervised models
• weather forecasting
• climate change
• data imbalance

Mentioned in this episode

Organizations: Communications Earth & Environment

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