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Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
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A large-scale solar dynamics observatory image dataset for computer vision applications.

Ahmet Kucuk1, Juan M Banda1, Rafal A Angryk1

  • 1Department of Computer Science, Georgia State University Atlanta 30302-3987, USA.

Scientific Data
|August 3, 2017
PubMed
Summary

This study compiles NASA

Keywords:
Computer scienceSolar physics

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Area of Science:

  • Solar Physics and Astrophysics
  • Computer Vision and Machine Learning

Background:

  • The National Aeronautics Space Agency (NASA) Solar Dynamics Observatory (SDO) mission provides vast amounts of solar image data.
  • Existing solar event detection research faces challenges due to data acquisition and curation from multiple sources.

Purpose of the Study:

  • To create a standardized, large-scale dataset of solar events from SDO imagery for the computer vision community.
  • To accelerate computer vision research on solar physics data by providing a curated, single-source repository.

Main Methods:

  • Compilation of SDO solar data into a unified repository.
  • Curation of several hundred thousand solar events from high-resolution solar images.
  • Provision of the dataset and source code for public access.

Main Results:

  • A comprehensive, curated dataset of solar events derived from SDO imagery.
  • Reduced data acquisition and curation time for researchers.
  • Enhanced quality of data for improved analysis and model development.

Conclusions:

  • The curated dataset and source code will significantly advance computer vision research in solar physics.
  • Anticipates wider adoption and interdisciplinary interest from both computer vision and solar physics communities.
  • Facilitates more efficient and effective detection and analysis of solar events.