Articles | Volume 9, issue 2
https://doi.org/10.5194/gi-9-267-2020
https://doi.org/10.5194/gi-9-267-2020
Research article
 | 
09 Jul 2020
Research article |  | 09 Jul 2020

Auroral classification ergonomics and the implications for machine learning

Derek McKay and Andreas Kvammen

Data sets

Replication Data for: Auroral Image Classification with Deep Neural Networks Andreas Kvammen, Kristoffer Wickstrøm, Derek McKay, and Noora Partamies https://doi.org/10.18710/SSA38J

Kiruna All-Sky Camera Urban Brändström http://www2.irf.se/allsky/data.html

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Short summary
Researchers are making increasing use of machine learning to improve accuracy, efficiency and consistency. During such a study of the aurora, it was noted that biases or distortions had crept into the data because of the conditions (or ergonomics) of the human trainers. As using machine-learning techniques in auroral research is relatively new, it is critical that such biases are brought to the attention of the academic and citizen science communities.