Using STAC to catalog machine learning training data.
Researchers and data scientists are increasingly combining Earth observation (EO) with ground truth data from a variety of sources to build faster, more accurate machine learning (ML) models to gain valuable insights in domains ranging from agriculture to autonomous navigation to ecosystem health monitoring. These models are integrated into analytic pipelines that generate on-the-fly predictions at scale. The accuracy of these inferences are then evaluated using well-defined validation metrics and the results used to improve the performance of the original model in a continuous feedback loop.
If this sounds like a complex process, that’s because it is! Ad-hoc techniques for handling these workflows may work well within a single organization, but can lead to a bewildering array of algorithms and data for end-users.