1,1,2,- 1Centre for mathematical Plasma Astrophysics (CmPA), KULeuven, Leuven, Belgium (panagiotis.gonidakis@kuleuven.be)
- 2Institute of Physics, University of Maria Curie-Sklodowska, Pl. Marii Curie-Sklodowskiej, 20-031 Lublin, Poland
- 3Solar-Terrestrial Centre of Excellence, Royal Observatory of Belgium, Avenue Circulaire 3, 1180 Uccle, Belgium
Automated identification of coronal structures using machine-learning techniques can support forecasting of extreme solar events, enable autonomous solar-observing missions, and accelerate understanding of physical processes in the solar atmosphere. Existing approaches typically focus on large-scale regions or adopt conservative segmentation strategies that limit structural detail. We train a lightweight variant of the You-Only-Look-Once (YOLO) object-detection framework [1] and, in parallel, design a scheme based on classical computer-vision operations and morphological filtering. Both are compared against the deep-learning-based SCSS-Net [2]. All three frameworks detect active regions and coronal holes in images from the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory. To reduce bias, training and testing use masks from multiple sources, including SPoCA [3], CHIMERA [4], Region Growth [5], and custom annotations. Methods are evaluated for scientific performance and computational cost using standard metrics such as the Dice score and Intersection over Union (IoU). We further assess on-board feasibility by outlining potential use cases and current technical limitations, and by evaluating performance on raw, uncalibrated data to ensure operational compatibility and robustness. Finally, we examine coronal hole mapping across multiple AIA wavelength channels and analyse correlations with signed and unsigned magnetic flux.
References
[1] Redmon et al. "You only look once: Unified, real-time object detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[2] Mackovjak et al. "SCSS-Net: solar corona structures segmentation by deep learning." Monthly Notices of the Royal Astronomical Society 508.3 (2021): 3111-3124.
[3] Verbeeck et al. "The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images." Astronomy & Astrophysics 561 (2014): A29.
[4] Garto et al. "Automated coronal hole identification via multi-thermal intensity segmentation." Journal of Space Weather and Space Climate 8 (2018): A02.
[5] Tlatov, A., K. Tavastsherna, and V. Vasil’eva. "Coronal holes in solar cycles 21 to 23." Solar Physics 289.4 (2014): 1349-1358.
How to cite: Gonidakis, P., Poedts, S., and Magdalenic, J.: Machine Learning for Solar Coronal Structure Segmentation on SDO AIA Data and Applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11886, https://doi.org/10.5194/egusphere-egu26-11886, 2026.
