Ahmadzadeh Azim, Adhyapak Rohan, Chaurasiya Kartik, Nagubandi Laxmi Alekhya, Aparna V, Martens Petrus C, Pevtsov Alexei, Bertello Luca, Pevtsov Alexander, Douglas Naomi, McDonald Samuel, Bawa Apaar, Kang Eugene, Wu Riley, Kempton Dustin J, Abdelkarem Aya, Copeland Patrick M, Seelamneni Sri Harsha
University of Missouri-St. Louis, Department of Computer Science, St. Louis, MO, 63121, USA.
Georgia State University, Department of Computer Science, Atlanta, GA, 30303, USA.
Sci Data. 2024 Sep 27;11(1):1031. doi: 10.1038/s41597-024-03876-y.
We present the Manually Annotated GONG Filaments in H-alpha Observations (MAGFiLO v1.0) dataset. This dataset contains 10,244 annotated filaments from 1,593 observations captured by the Global Oscillation Network Group (GONG), spanning the years 2011 through 2022. Each annotation details one filament's segmentation, minimum bounding box, spine, and magnetic field chirality. With a total of over one thousand person-hours of annotation, and a double-blind review process, we ensured high-quality ground-truth data. Our inter-annotator agreement reaches a Kappa score of 0.66. We also verified that the hemispheric preference of filaments as annotated in MAGFiLO aligns with the findings from similar datasets of much smaller sample sizes. MAGFiLO is the first dataset of its size, enabling advanced deep learning models to identify filaments and their features with unprecedented precision. It also provides a testbed for solar physicists interested in large-scale analysis of filaments. In this report, we document the details of the annotation and the post-processing phases that were applied.
我们展示了H-α波段观测中的人工标注太阳细丝数据集（MAGFiLO v1.0）。该数据集包含来自全球振荡网络组（GONG）在2011年至2022年期间捕获的1593次观测中的10244条标注细丝。每个标注详细说明了一条细丝的分割、最小包围盒、脊线和磁场手征性。经过总计超过一千人时的标注以及双盲评审过程，我们确保了高质量的地面真值数据。我们的标注者间一致性达到了0.66的卡帕分数。我们还验证了MAGFiLO中所标注细丝的半球偏好与样本量小得多的类似数据集的研究结果一致。MAGFiLO是同类数据集中首个如此规模的数据集，使先进的深度学习模型能够以前所未有的精度识别细丝及其特征。它还为对细丝进行大规模分析感兴趣的太阳物理学家提供了一个试验台。在本报告中，我们记录了所应用的标注和后处理阶段的详细信息。
