An initiative of the European Commission

AI in Earth Sciences​

SCIANCE is gathering evidence on scientific challenges, AI capabilities, infrastructures and emerging practices in “AI in Earth Sciences​”​, supporting the identification of strategic priorities for RAISE.

Context and scope​

Earth sciences investigate the processes shaping the Earth system across the atmosphere, oceans, land surface and biosphere. This review focuses on three interconnected research domains where AI is increasingly used to improve understanding, prediction and decision-making: climate and weather sciences, agriculture and soil sciences, and biodiversity and ecosystem services. Together, these domains address challenges related to environmental change, extreme weather events, sustainability, food systems, natural resources and planetary resilience, while relying on increasingly complex observations, simulations and digital infrastructures.

State-of-the-Art Insights​

Scientific research challenges​

  • Understanding interconnected Earth-system processes
  • Assessing climate variability and predicting extreme events
  • Improving food-system sustainability and soil health
  • Monitoring biodiversity loss and ecosystem degradation
  • Supporting evidence-based environmental management and decision-making

Structural barriers​

  • Fragmented datasets and inconsistent observation standards​
  • Limited interoperability across environmental domains and services​
  • Significant computing and infrastructure requirements
  • Uncertainty, explainability and model transferability challenges
  • Governance requirements for policy and decision-support applications​

AI Approaches and Maturity​

  • Operational: Weather forecasting, Earth Observation analytics, crop monitoring, yield prediction, digital soil mapping and environmental monitoring
  • Emerging: Biodiversity monitoring, climate-risk forecasting, ecosystem intelligence and hybrid process-AI models.
  • Frontier: Earth-system foundation models, environmental digital twins and integrated climate-biodiversity intelligence systems.​

European ecosystem​

Europe has a unique ecosystem of infrastructures and initiatives including Copernicus, Destination Earth, EOSC, EUSO and major biodiversity and environmental monitoring networks. These assets provide world-leading observation, modelling, computing and data-management capabilities for AI-enabled Earth Sciences.

Emerging trends​

  • Earth-system foundation models
  • Environmental digital twins
  • Hybrid AI and process-based Earth-system modelling
  • AI-powered early-warning systems
  • Integrated climate-biodiversity analysis
  • Interoperable environmental data spaces

Outlook

AI is becoming an increasingly important component of environmental science. Future advances will depend on integrating observations, models and infrastructures into trustworthy, interoperable systems capable of supporting both scientific understanding and societal resilience.

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