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.