TY - GEN
T1 - Deep Computer Vision for Solar Physics Big Data
T2 - 2024 IEEE International Conference on Big Data, BigData 2024
AU - Shen, Bo
AU - Marena, Marco
AU - Li, Chenyang
AU - Li, Qin
AU - Jiang, Haodi
AU - Du, Mengnan
AU - Xu, Jiajun
AU - Wang, Haimin
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Big Data
KW - Deep Computer Vision
KW - Solar Physics
UR - http://www.scopus.com/inward/record.url?scp=85218058468&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85218058468&partnerID=8YFLogxK
U2 - 10.1109/BigData62323.2024.10825648
DO - 10.1109/BigData62323.2024.10825648
M3 - Conference contribution
AN - SCOPUS:85218058468
T3 - Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
SP - 1860
EP - 1864
BT - Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
A2 - Ding, Wei
A2 - Lu, Chang-Tien
A2 - Wang, Fusheng
A2 - Di, Liping
A2 - Wu, Kesheng
A2 - Huan, Jun
A2 - Nambiar, Raghu
A2 - Li, Jundong
A2 - Ilievski, Filip
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 December 2024 through 18 December 2024
ER -