用于太阳物理大数据的深度计算机视觉：机遇与挑战
Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges
AI总结：
该愿景论文梳理太阳物理大数据的类型与特征，探讨深度计算机视觉在其中的应用机遇、独特挑战及未来研究方向。
摘要：
随着近期各项任务的开展，例如 Solar Dynamics Observatory（SDO，太阳动力学天文台）和 Parker Solar Probe（帕克太阳探测器）等先进天基观测台，以及 Daniel K. Inouye Solar Telescope（DKIST，丹尼尔·井上太阳望远镜）等地基望远镜，数据的规模、产生速度和多样性已使太阳物理进入一个变革性时代，即太阳物理大数据（SPBD）。随着深度计算机视觉的最新发展，SPBD 在解决以往无法解决的问题方面出现了新的机遇。然而，由于 SPBD 和深度计算机视觉模型的内在特征，也产生了新的挑战。本愿景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 的新机遇，强调了其独特挑战，并提出了若干潜在的未来研究方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
