A foundation model is a large-scale AI system trained on vast and varied datasets. A familiar example is ChatGpt, which learns from text. Our model, by contrast, is trained on data from four different sensors onboard Sentinel 1, 2 & 3 satellites, combining radar and multispectral imagery.
Unlike traditional task-specific models, which must be trained separately for each application, a foundation model can generalise across multiple monitoring tasks. Through self-supervised learning, this reduces the need for manually labelled datasets and can be fine-tuned quickly to handle various environmental challenges.
We are developing a foundation model for Earth observation (EO) to replace today’s application specific artificial intelligence (AI) models for analysing EO data. By training a single, adaptable model on diverse satellite data, our model enables faster, more scalable insights across a variety of environmental tasks, such as flood detection, drought- and sea ice monitoring. Faster, more efficient use of AI in environmental monitoring.