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

TL;DR

Classi-Fly leverages open air traffic data and machine learning to accurately infer aircraft categories and detect specific flight types, enhancing metadata for various applications.

Contribution

It introduces a novel machine learning approach, Classi-Fly, for inferring aircraft metadata from movement patterns using open data sources.

Findings

Achieves over 88% accuracy in aircraft category classification

Demonstrates feasibility of detecting police and surveillance flights

Validates approach with large-scale open source flight data

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.