Ease QC – Development of a Service to detect anomalies in Earth Observation data using AI (Artificial Intelligence) models

EASEQC aims at expanding the use of AI/ML for quality control of EO products. The traditional approach to quality control, usually involving deterministic models together with considerable manual intervention, is no longer feasible given increasing data volumes of EO data archives. ML/AI has potential to make the process of quality control more efficient. EASEQC focuses on the development of semi-supervised ML models for detection anomalies in EO products. This entails that models can be trained with limited training data and that a model is capable of identifying generally anomalous data products i.e. different anomalies can be detected by the same model. The service is being implemented in a cloud environment and will be fully accessible via an API.