Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges

TL;DR

This paper reviews how deep computer vision can address challenges in solar physics big data, highlighting opportunities, obstacles, and future research directions in analyzing large-scale solar observational data.

Contribution

It provides an overview of applying deep computer vision techniques to solar physics big data, identifying challenges and proposing future research avenues.

Findings

Deep computer vision offers new solutions for solar physics data analysis.

Challenges include data heterogeneity and model adaptation.

Future research directions are outlined for advancing the field.

Abstract

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.