An automated auroral detection system using deep learning: real-time operation in Tromsø, Norway

DOI DOI HANDLE PDF Web Site ほか1件をすべて表示 一部だけ表示 被引用文献2件 参考文献45件 オープンアクセス

書誌事項

公開日
2022-05-31
資源種別
journal article
権利情報
  • https://creativecommons.org/licenses/by/4.0
  • https://creativecommons.org/licenses/by/4.0
DOI
  • 10.1038/s41598-022-11686-8
  • 10.21203/rs.3.rs-1090985/v1
公開者
Springer Science and Business Media LLC

説明

<jats:title>Abstract</jats:title><jats:p>The activity of citizen scientists who capture images of aurora borealis using digital cameras has recently been contributing to research regarding space physics by professional scientists. Auroral images captured using digital cameras not only fascinate us, but may also provide information about the energy of precipitating auroral electrons from space; this ability makes the use of digital cameras more meaningful. To support the application of digital cameras, we have developed artificial intelligence that monitors the auroral appearance in Tromsø, Norway, instead of relying on the human eye, and implemented a web application, “Tromsø AI”, which notifies the scientists of the appearance of auroras in real-time. This “AI” has a double meaning: artificial intelligence and eyes (instead of human eyes). Utilizing the Tromsø AI, we also classified large-scale optical data to derive annual, monthly, and UT variations of the auroral occurrence rate for the first time. The derived occurrence characteristics are fairly consistent with the results obtained using the naked eye, and the evaluation using the validation data also showed a high F1 score of over 93%, indicating that the classifier has a performance comparable to that of the human eye classifying observed images.</jats:p>

収録刊行物

  • Scientific Reports

    Scientific Reports 12 (1), 8038-, 2022-05-31

    Springer Science and Business Media LLC

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