Sentinel-2 New Anomalies AI4QC

This dataset was used in the AI4QC project (Artificial Intelligence for Quality Control), in the context of the detection of new anomalies through unsupervised learning (unlabeled data). It consists of 6452 Sentinel-2 images (true color images in jpg format). The dataset was divided into training and testing folders (80% training and 20% testing). Two criterias were considered for the train/test split: seasonality and geographic location. An additional folder, "S2_additional_data" contains 61 more products which were flagged as anomalous by the MPC. This data is not included in the train/test folders but can be used if one wishes to increase the amount of S2 products.