  Every moment, countless meteoroids enter our atmosphere unseen. The detection
and measurement of meteors offer the unique opportunity to gain insights into
the composition of our solar systems' celestial bodies. Researchers, therefore,
carry out a wide-area-sky-monitoring to secure 360-degree video material,
saving every single entry of a meteor. Existing machine intelligence cannot
accurately recognize events of meteors intersecting the earth's atmosphere due
to a lack of high-quality training data publicly available. This work presents
four reusable open source solutions for researchers trained on data we
collected due to the lack of available labeled high-quality training data. We
refer to the proposed dataset as the NightSkyUCP dataset, consisting of a
balanced set of 10,000 meteor- and 10,000 non-meteor-events. Our solutions
apply various machine learning techniques, namely classification, feature
learning, anomaly detection, and extrapolation. For the classification task, a
mean accuracy of 99.1\% is achieved. The code and data are made public at
figshare with DOI: 10.6084/m9.figshare.16451625
