Dataset for auto identification of atmospheric disturbances and key parameter extraction based on all-sky airglow imaging observations of the Chinese Meridian Project

A convolutional neural network classification model is used to screen images under clear night sky conditions, and the faster region-based convolutional neural network is applied to locate wave structures.

For atmospheric gravity waves, a method based on two-dimensional Fourier transform is adopted to extract wavelength, propagation direction, and horizontal velocity, while for mesoscale traveling ionospheric disturbances, Canny edge detection combined with linear fitting is used to extract wave parameters.