Optimising the data path for Earth-observation inference at scale

Tue, 14 Jul 2026 14:00:00 +0200

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.

Data Flow on DaFAB

Optimising the data path for Earth-observation inference at scale