Volume 45 Issue 6
Dec.  2025
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Article Navigation >  Chinese Journal of Space Science  > 2025  >  45(6): 1650-1655 Open Access
PAN Jinhui, LIU Jiajia, LIU Rui. Dataset of Solar Active Regions in the Solar Full-disk Magnetograms (in Chinese). Chinese Journal of Space Science, 2025, 45(6): 1650-1655 doi: 10.11728/cjss2025.06.2025-0086
Citation: PAN Jinhui, LIU Jiajia, LIU Rui. Dataset of Solar Active Regions in the Solar Full-disk Magnetograms (in Chinese). Chinese Journal of Space Science, 2025, 45(6): 1650-1655 doi: 10.11728/cjss2025.06.2025-0086
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Dataset of Solar Active Regions in the Solar Full-disk Magnetograms

doi: 10.11728/cjss2025.06.2025-0086 cstr: 32142.14.cjss.2025-0086
  • 1.

    School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026

  • 2.

    National Key Laboratory of Deep Space Exploration, Hefei 230026

  • Received Date: 2025-06-02
  • Rev Recd Date: 2025-09-26
    • Available Online: 2025-09-26
    Abstract
  • Solar active regions are key source regions of intense solar phenomena such as solar flares and Coronal Mass Ejections (CMEs). Accurate identification of these regions can help forecast the impact of solar activities on the geospace environment. This dataset utilizes solar full-disk line-of-sight magnetograms observed by the Helioseismic and Magnetic Imager onboard the Solar Dynamic Observatory (2010－2019), combined with NOAA AR numbers provided by the Solar Monitor website (https://solarmonitor.org/). The active regions are annotated using an image processing-based recognition method along with manual labeling in accordance with the NOAA catalog. The dataset consists of 6975 solar full-disk magnetograms, taken every 12 hours, with a total number of 26531 annotations consisting of the active-region information for each magnetogram. This dataset serves as a benchmark resource for developing deep learning solar active region detection models, and aims to enhance predictive capabilities for severe space weather phenomena through physics-informed training samples.

     

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