For those of you who might be interested, recently I developed an app (SkyCondition) that classifies the sky condition by processing images streamed from an all-sky camera. It is a ML/AI approach, which is quite effective.
In my observatory I use an indi-allsky system as the image server, but the app can be integrated with any all-sky system that serves the latest image to a give file location. The output is a text file, updated periodically, with the sky condition classified in four classes (Clear, Cloudy, Covered, and Rainy), and a session recommendation (GO, PAUSE, STOP) based on some user settings.
The text file can be read an parsed by a Safety Monitor of sorts. I have integrated mine with NINA, using the ASCOM Generic File Safety Monitor Driver, and it works very nicely.
This is my first version, and have plans for further improvements. It comprises of an .exe file that runs in Windows, and an .h5 file that contains the neural net data. Both are provided in the link bellow. Note that the .exe file is quite large, as it bundles all of the dependencies so that it can run in any installation.
Please visit http://www.pampaskie...tion-Detection for more details, and a few examples (including timelapse labeled videos that demonstrate the performance of the approach).
Note this is a work-in-progress, but already very usable (at your own risk, of course).