Whether it’s for monitoring floods, assessing agricultural conditions, or detecting wildfires, every Earth-Observation AI system based on satellite remote-sensing data shares a common task: given the area of interest and a time window, find the products that cover it and turn them into model inputs.

Intuitively, a live discovery approach appears attractive: products are discovered, retrieved, and prepared as part of each model run, avoiding the complexity of maintaining a separate data layer.

Workflows processing Earth Observation (EO) data have a problem – the body of available EO data is vast. And growing rapidly. Within the DaFab EU project, AI-driven workflows must process massive quantities of EO data, made available by the Copernicus project, in an efficient and reliable manner. This presents a range of problems, including locating the relevant data, decoupling relatively fast and scalable compute tasks from slower data transfers, storing the data in a way that the workflows can use it, and managing the lifetime of any temporary copies required. This is where DASI (the Data Access and Storage Interface) plays a critical role. It provides the smart bridge between storage systems and compute environments. DASI’s semantically driven data management design helps build intelligent, scalable, and optimized AI workflows in the DaFab project.