Classification of flight types from GPS recordings

Anže Kolar (2018) Classification of flight types from GPS recordings. EngD thesis.

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Abstract

Flight loggers that store GPS positions are becoming increasingly popular. Records created with such devices are usually uploaded to various web platforms that provide methods for further data exploration and integrate services for sharing the captured flights on the social media. The amount of uploaded files is continually increasing, thus creating the need for smarter classification of the data, which in turn creates a more optimised and user-friendly service. The goal of this thesis is to develop a system for the recognition of the aircraft type in which the flight was recorded. We define a set of attributes on a smaller subset of the entire flight database and use it for building and comparing the models created using different methods of machine learning. We also present a few methods that could further expand the original training set. Final results are mostly positive: most promising models achieve an F1 score of more than 0.97 which makes them suitable for the use in a production environment. Even better scores can be attained by increasing the number of learning samples.

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