2025-09-03, 13:30–15:00 (Europe/Amsterdam), Expert Room 2
This summer school session will introduce participants to a Python/Dask-based ecosystem, familiarizing them with the use of the Remote Sensing Deployable Analysis environmenT (RS-DAT) framework to scale EO and RS data analysis using HTC/HPC systems and associated storage resources. The session will cover the tools for data access, retrieval and storage, and demonstrate the scaling up of processing and analysis workflows focused on EO data-sets.
Data volumes are increasing across the natural and engineering sciences, with this being particularly relevant in the context of remote-sensing (RO) and Earth observation (EO) data, which have become a mainstay in fields ranging from the geosciences to ‘green’ life sciences, agriculture, and even social sciences, as well as an invaluable tool in defining policy.
Although a community driven software ecosystem has evolved to support exploitation of these data, existing and future workflows often must be scaled up beyond the computational and storage resources available in workstations, taxing the tooling available and presenting a challenge to the use of these data.
In this regard, solutions using high-throughput and high-performance computing (HTC/HPC) systems, as an additional alternative to cloud-based solutions, are of particular relevance for the academic community. Offering full control over available hardware, software, and data, these systems are excellently suited to highly-customized academic workflows and can readily support the migration of existing workflows. Furthermore, they are generally available through national infrastructure providers on a merit-driven no-cost basis.
This summer school session will introduce participants to a Python/Dask-based ecosystem, familiarizing them with the use of the Remote Sensing Deployable Analysis environmenT (RS-DAT) framework to scale EO and RS data analysis using HTC/HPC systems and associated storage resources. The session will cover the tools for data access, retrieval and storage, and demonstrate the scaling up of processing and analysis workflows focused on EO data-sets.