Foundation Models (FMs) are set to revolutionize domains like Earth Observation (EO) and Earth Sciences. Trained on vast unlabeled datasets via self-supervised learning, they can uncover complex patterns and latent information. Once pre-trained, Geospatial FMs can be adapted to diverse tasks with minimal fine-tuning or additional data. As a result, this paradigm shift is set to reshape the entire information value chain, with far-reaching implications for industry, research and development, and the broader scientific community.
This session aims to share the latest research and technological advances and discuss practical solutions for effectively integrating FMs into the Earth Observation and Earth Sciences ecosystems. We encourage interdisciplinary collaboration, and submissions from AI researchers, EO and Earth data scientists and industry experts, as well as from stakeholders from High-Performance Computing (HPC), Big Data, and EO application communities.
The main topics for the session are:
● Latest Advances in AI Foundation Models: FMs can process data from various sensors, including multi- or hyper-spectral, SAR, LiDAR, and more, enabling comprehensive analysis of the Earth's dynamics holistically. Recent progress marks a shift from sensor-specific models toward sensor-aware or sensor-agnostic architectures.
● Benchmarking and Evaluating Foundation Models: Establishing standardised fair evaluation metrics and benchmarks to assess the performance and capabilities of FMs, ensuring reliability and efficiency, moving beyond simplistic or canonical use cases.
● Embedding and Geospatial Semantic Data Mining: FMs enable advanced geospatial semantic mining by leveraging latent space embeddings to uncover meaningful patterns and relationships. This enhances interpretation while reducing the need for large volumes of raw data across time and space.
● Implications of Foundation Models for the Community: Understanding the potential societal, environmental, and economic impacts of FMs, fostering informed decision-making and resource management. Seamless integration with downstream systems such as digital twins, public dashboards, and early warning platforms, including deployment at the edge (e.g. onboard satellites) is essential. Emerging roles of Agentic AI, in synergy with Large Language Models (LLMs) open new pathways for autonomous, context-aware EO applications.