  Computing power, big data, and advancement of algorithms have led to a
renewed interest in artificial intelligence (AI), especially in deep learning
(DL). The success of DL largely lies on data representation because different
representations can indicate to a degree the different explanatory factors of
variation behind the data. In the last few year, the most successful story in
DL is supervised learning. However, to apply supervised learning, one challenge
is that data labels are expensive to get, noisy, or only partially available.
With consideration that we human beings learn in an unsupervised way;
self-supervised learning methods have garnered a lot of attention recently. A
dominant force in self-supervised learning is the autoencoder, which has
multiple uses (e.g., data representation, anomaly detection, denoise). This
research explored the application of an autoencoder to learn effective data
representation of helicopter flight track data, and then to support helicopter
track identification. Our testing results are promising. For example, at
Phoenix Deer Valley (DVT) airport, where 70% of recorded flight tracks have
missing aircraft types, the autoencoder can help to identify twenty-two times
more helicopters than otherwise detectable using rule-based methods; for Grand
Canyon West Airport (1G4) airport, the autoencoder can identify thirteen times
more helicopters than a current rule-based approach. Our approach can also
identify mislabeled aircraft types in the flight track data and find true types
for records with pseudo aircraft type labels such as HELO. With improved
labelling, studies using these data sets can produce more reliable results.
