Speaker
Description
Solar filaments, phenomena in the solar corona, are of significant scientific interest due to their link with violent eruptive events such as coronal mass ejections. This study introduces a comprehensive deep learning framework for the detection, classification, segmentation, and tracking of solar filaments using H$\alpha$ images from the Global Oscillation Network Group data archive. Using together a DETR-based model for detection and classification, a U-Net for instance segmentation, and a custom-made tracking algorithm, we achieved state of the art performance across all tasks, overcoming typical challenges. In addition, we introduce a new dataset with detailed classifications and segmentations of solar filaments in H$\alpha$ images, with a focus on space weather studies. The proposed methodology significantly advances solar filament analysis, offering improved capabilities for automated studies and potential applications in space weather prediction.
