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arXiv · 1908.01061

Classi-Fly: Inferring Aircraft Categories from Open Data using Machine Learning

Martin Strohmeier·Matthew Smith·Vincent Lenders·Ivan Martinovic
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Abstract

In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of information about military and government movements. Typically these applications require knowledge about the metadata of the aircraft, specifically its operator and the aircraft category. armasuisse Science + Technology, the R\&D agency for the Swiss Armed Forces, has been developing Classi-Fly, a novel approach to obtain metadata about aircraft based on their movement patterns. We validate Classi-Fly using several hundred thousand flights collected through open source means, in conjunction with ground truth from publicly available aircraft registries containing more than two million aircraft. Classi-Fly obtains the correct aircraft category with an accuracy of over 88%, demonstrating that it can improve the meta data necessary for applications working with air traffic communication. Finally, we show that it is feasible to automatically detect specific flights such as police and surveillance missions.

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Martin Strohmeier, Matthew Smith, Vincent Lenders, Ivan Martinovic. 2019-07-30. Classi-Fly: Inferring Aircraft Categories from Open Data using Machine Learning. https://arxiv.org/abs/1908.01061

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