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

• Published in: Scientific Reports
• Main Authors: Sota Nanjo, Satonori Nozawa, Masaki Yamamoto, Tetsuya Kawabata, Magnar G. Johnsen, Takuo T. Tsuda, Keisuke Hosokawa
• Format: Article in Journal/Newspaper
• Language: English
• Published: Nature Portfolio 2022
• Subjects: Medicine; R; Science; Q; Norway; Tromsø
• Online Access: https://doi.org/10.1038/s41598-022-11686-8; https://doaj.org/article/4dbee1b0faf4462692a49c94f3b59044

Abstract 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.