Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges
It identifies potential advancements in solar physics research through deep learning, but is incremental as it reviews existing concepts without presenting new results.
This vision paper explores the application of deep computer vision to solar physics big data (SPBD), highlighting opportunities for solving previously unsolvable problems and addressing challenges due to data characteristics and model limitations.
With recent missions such as advanced space-based observatories like the Solar Dynamics Observatory (SDO) and Parker Solar Probe, and ground-based telescopes like the Daniel K. Inouye Solar Telescope (DKIST), the volume, velocity, and variety of data have made solar physics enter a transformative era as solar physics big data (SPBD). With the recent advancement of deep computer vision, there are new opportunities in SPBD for tackling problems that were previously unsolvable. However, there are new challenges arising due to the inherent characteristics of SPBD and deep computer vision models. This vision paper presents an overview of the different types of SPBD, explores new opportunities in applying deep computer vision to SPBD, highlights the unique challenges, and outlines several potential future research directions.