High Performance Computing (HPC) enables precise analysis of large and complex Earth Observation (EO) datasets. However, the adoption of supercomputing in the EO community faces challenges from the increasing heterogeneity of HPC systems, limited expertise, and the need to leverage novel computing technologies. This paper explores the implications of exascale computing advancements and the inherent heterogeneity of HPC architectures. It highlights EU-supported projects optimizing software development and harnessing the capabilities of heterogeneous HPC configurations. Methodologies addressing challenges of modular supercomputing, large-scale Deep Learning (DL) models, and hybrid quantum-classical algorithms are presented, aiming to enhance the utilization of supercomputing in EO for improved research, industrial applications, and SME support.
In this PhD, you will develop AI methods to enhance the accessibility and analysis of a vast amount of high-resolution, multi-source EO data. You will research ML methods of encode and compress high-volume EO data into practical, much smaller representations and scale them to large datasets on our supercomputers.