This session aims to showcase novel applications of methods from the field 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 nowadays for scientific purposes, for example when dealing with “big data” from Global Navigation Satellite System (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. Strategies and methodologies from the fields of artificial intelligence and machine learning have shown great potential not only in this context but also when applied to more limited data sets to solve complex non-linear problems in geodesy.
We invite contributions related to various aspects of applying methods from artificial intelligence and machine learning (including both shallow and deep learning techniques) to geodetic problems and data sets. 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 data sets, improved predictions of geodetic parameters, such as Earth orientation or atmospheric parameters into the future, combination and extraction of information from multiple inhomogeneous data sets (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, interpretability, and explainability of machine learning outcomes, as well as the integration of physical relationships into data-driven frameworks.
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.
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 nowadays for scientific purposes, for example when dealing with “big data” from Global Navigation Satellite System (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. Strategies and methodologies from the fields of artificial intelligence and machine learning have shown great potential not only in this context but also when applied to more limited data sets to solve complex non-linear problems in geodesy.
We invite contributions related to various aspects of applying methods from artificial intelligence and machine learning (including both shallow and deep learning techniques) to geodetic problems and data sets. 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 data sets, improved predictions of geodetic parameters, such as Earth orientation or atmospheric parameters into the future, combination and extraction of information from multiple inhomogeneous data sets (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, interpretability, and explainability of machine learning outcomes, as well as the integration of physical relationships into data-driven frameworks.
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.