RFI_AI4QC dataset

This dataset was used in the AI4QC project (Artificial Intelligence for Quality Control), in the context of RFI detection through an object detection task. It consists of a set of labeled RFIs (radio frequency interferences). These interferences are caused by man-made sources and can lead to an artefact in the satellite image, typically a bright rectangular pattern. Bounding boxes were defined around RFI artefacts in 3940 Sentinel-1 quick-looks (png images). A few "other anomalies" were identified as well, leaving a total of 11724 "RFI" bounding boxes and 301 "Other Anomalies" bounding boxes.

The labeled RFIs are available in three formats: PASCAL VOC (xml files), COCO (json files) and YOLO (txt files). Each is contained in a different zip file. The S1_images zip file contains the 3940 Sentinel-1 quick-looks. One can combine the label files (in a chosen format) with the S1 images to train object detection algorithms to automatically detect RFIs in a satellite image. A predefined train/test split is available (80% training and 20% testing), with training and testing zip folders containing the images and labels for each subset. The data was split according to 3 criterias: RFI over land vs sea, size of the RFI bounding boxes and geographic location.