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

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.

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Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/bigdata62323.2024.10825648
Citations
1
Primary Topic
Currency Recognition and Detection
Type
article
Field-Weighted Citation Impact
0.25
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article

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

Haodi Jiang, Haimin Wang, Mengnan Du, Bo Shen et al.
1 citations
Currency Recognition and Detection
0.25
article

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

Haodi Jiang, Haimin Wang, Mengnan Du, Bo Shen, Jiajun Xu, Chenyang Li, Li Qin, Marco Marena
article en
1 citations

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.

New Jersey Institute of Technology (US), Sam Houston State University (US), Menlo School (US)
Climate action
Openalex Percentile: Top 42%
Currency Recognition and Detection
0.25
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Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges [Vision Paper] — Haodi Jiang, Haimin Wang, et al. · (2024) | TGRS Research Map | TGRS