AI and Big Data in Earth Science

Aim

The “AI and Big Data in Earth Science” section in the journal Earth is dedicated to exploring the application of artificial intelligence (AI), machine learning (ML), and Big Data techniques to address complex challenges in earth and environmental sciences. This section aims to provide a platform for researchers to publish innovative studies that leverage AI, ML, and Big Data to advance our understanding of global environmental systems, improve predictive capabilities, and develop sustainable solutions. Key topics within this section include the use of AI and Big Data for climate change modeling and prediction, environmental monitoring and management, and natural hazard assessment and mitigation, and the development of new methodologies and tools for data analysis and interpretation. Additionally, this section welcomes research on the integration of AI, Big Data, and traditional earth science approaches to enhance our ability to manage natural resources, protect ecosystems, and promote environmental sustainability.

Scope

This section covers a wide range of applications and methodologies, including, but not limited to, the following:

• AI- and ML-based modeling of climate change and extreme weather events;
• Remote sensing data fusion and automated interpretation using deep learning;
• Big data analytics for geospatial, hydrological, atmospheric, and oceanographic data;
• Predictive modeling and early warning systems for natural hazards (e.g., wildfires, floods, earthquakes);
• Smart environmental monitoring and anomaly detection;
• AI for sustainable land, water, and energy resource management;
• Integration of physics-based models with AI (hybrid and physics-informed ML);
• Uncertainty quantification, explainable AI, and trustworthy ML in geoscience applications;
• Generative models and large-scale earth system simulations;
• Edge computing, sensor networks, and real-time data pipelines for environmental systems.

This section also welcomes contributions on the societal and ethical implications of AI applications in earth science, as well as discussions on data governance, accessibility, and equity.

• AI in geosciences
• machine learning
• deep learning
• big data
• environmental monitoring
• natural hazard prediction
• remote sensing; spatiotemporal modeling
• climate informatics
• earth system science
• sustainable resource management
• physics-informed ML
• explainable AI