Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges [Vision Paper]
Document Type
Conference Proceeding
Publication Date
1-1-2024
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 previously unsolvable problems. However, new challenges arise 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.
Identifier
85218058468 (Scopus)
ISBN
[9798350362480]
Publication Title
Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
External Full Text Location
https://doi.org/10.1109/BigData62323.2024.10825648
First Page
1860
Last Page
1864
Grant
80NSSC24M0174
Fund Ref
National Aeronautics and Space Administration
Recommended Citation
Shen, Bo; Marena, Marco; Li, Chenyang; Li, Qin; Jiang, Haodi; Du, Mengnan; Xu, Jiajun; and Wang, Haimin, "Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges [Vision Paper]" (2024). Faculty Publications. 702.
https://digitalcommons.njit.edu/fac_pubs/702
