This session showcases novel applications of artificial intelligence and machine learning in geodesy.

In recent years, the exponential growth of geodetic data from various observation techniques has created challenges and opportunities. Innovative approaches are required to efficiently handle and harness the vast amount of geodetic data available for scientific purposes, for example, when dealing with “big data” from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). Likewise, numerical weather models and other environmental models important for geodesy come with ever-growing resolutions and dimensions. Artificial intelligence and machine learning have shown great potential not only in this context but also when applied to more limited datasets to solve complex non-linear problems in geodesy.

We invite contributions related to applying methods from artificial intelligence and machine learning (including shallow, deep, and generative learning architectures, as well as geospatial foundation models) to geodetic problems and datasets. We welcome investigations related to (but not limited to): more efficient and automated processing of geodetic data, pattern and anomaly detection in geodetic time series, images, or higher-dimensional datasets, implicit neural representations for continuous spatial field modeling, improved predictions of geodetic parameters, such as Earth orientation, atmospheric, or hydrological parameters into the future, combination and extraction of information from multiple inhomogeneous datasets (multi-temporal, multi-sensor, multi-modal fusion), feature selection and sensitivity, downscaling geodetic data, and improvements of large-scale simulations. We strongly encourage contributions that address crucial aspects of uncertainty quantification and integration of physical relationships into data-driven frameworks (physics-informed machine learning). Moreover, reproducibility, interpretability, and explainability of machine learning outcomes are fundamental to ensuring the scientific rigor of novel AI-based solutions.

By combining the power of artificial intelligence with geodetic science, we aim to open new horizons in our understanding of Earth's dynamic geophysical processes. Join us in this session to explore how the fusion of physics and machine learning promises advantages in generalization, consistency, and extrapolation, ultimately advancing the frontiers of geodesy.