标题： 太阳物理大数据的深度计算机视觉：机遇与挑战 显示英文标题

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

摘要： 随着最近的任务，例如先进的基于空间的观测台，如太阳动力学天文台（SDO）和帕克太阳探测器，以及地基望远镜，如丹尼尔·K·井上太阳望远镜（DKIST），数据量、速度和种类使太阳物理学进入了一个变革的时代，即太阳物理大数据（SPBD）。 随着深度计算机视觉的最新进展，在SPBD中出现了解决之前无法解决的问题的新机会。 然而，由于SPBD和深度计算机视觉模型的固有特性，也出现了一些新的挑战。 这篇展望论文概述了不同类型的SPBD，探讨了将深度计算机视觉应用于SPBD的新机会，强调了独特的挑战，并概述了几个潜在的未来研究方向。 显示英文摘要

摘要： 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.