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

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

Fine‐scale visualization of aurora in a wide area using color digital camera images from the international space station

Abstract The full‐color photographs of aurora have been taken with digital single‐lens reflex cameras mounted on the International Space Station (ISS). Since these photographs do not have accurate time and geographical information, in order to use them as scientific data, it is necessary to calibrate the imaging parameters (such as looking direction and angle of view of the camera) of the photographs. For this purpose, we calibrated the imaging parameters using a city light image taken from the Defense Meteorological Satellite Program satellite following the method of Hozumi et al. (2016, https://doi.org/10.1186/s40623-016-0532-z). We mapped the photographs onto the geographic coordinate system using the calibrated imaging parameters. To evaluate the accuracy of the mapping, we compared the aurora taken simultaneously from ISS and ground. Comparing the spatial structure of discrete aurora and the temporal variation of pulsating aurora, the accuracy of the data set is less than 0.3 s in time and less than 5 km in space in the direction perpendicular to the looking direction of the camera. The generated data set has a wide field of view (~ 1,100 × 900 km), and their temporal resolution is less than 1 s. Not only that, the field of view can sweep a wide area (~ 3,000 km in longitude) in a short time (~ 10 min). Thus, this new imaging capability will enable us to capture the evolution of fine‐scale spatial structure of aurora in a wide area