The CHASM-SWPC Dataset for Coronal Hole Detection & Analysis
Cutter Beck, Evan Smith, Khagendra Katuwal, Rudra Kafle, Jacob Whitehill

TL;DR
This paper introduces the CHASM-SWPC dataset for coronal hole detection, developed through semi-automatic annotation of solar EUV maps, and demonstrates improved neural network performance using this dataset.
Contribution
The paper presents a new high-quality dataset and annotation tool for coronal hole detection, along with enhanced neural network models trained on this data.
Findings
Neural networks trained on CHASM-SWPC achieved 0.9805 accuracy.
The dataset enabled better model performance than previous pretrained models.
The CHASM tool facilitated rapid and accurate annotation of solar maps.
Abstract
Coronal holes (CHs) are low-activity, low-density solar coronal regions with open magnetic field lines (Cranmer 2009). In the extreme ultraviolet (EUV) spectrum, CHs appear as dark patches. Using daily hand-drawn maps from the Space Weather Prediction Center (SWPC), we developed a semi-automated pipeline to digitize the SWPC maps into binary segmentation masks. The resulting masks constitute the CHASM-SWPC dataset, a high-quality dataset to train and test automated CH detection models, which is released with this paper. We developed CHASM (Coronal Hole Annotation using Semi-automatic Methods), a software tool for semi-automatic annotation that enables users to rapidly and accurately annotate SWPC maps. The CHASM tool enabled us to annotate 1,111 CH masks, comprising the CHASM-SWPC-1111 dataset. We then trained multiple CHRONNOS (Coronal Hole RecOgnition Neural Network Over…
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Taxonomy
TopicsSolar and Space Plasma Dynamics · Ionosphere and magnetosphere dynamics · Earthquake Detection and Analysis
