Cross-Aircraft Flight Phase Classification Using ADS-B Data and Transfer Learning

Abstract

Flight phase identification (FPI) approaches that apply traditional machine learning techniques are expensive to scale, difficult to generalize across platforms, and frequently unavailable in permissive or distributed training environments. We propose a scalable, data-driven pipeline for automatic FPI using open-source Automatic Dependent Surveillance-Broadcast (ADS-B) data, with an emphasis on cross-aircraft generalization through transfer learning. Leveraging ADS-B telemetry from USAF Initial Flight Training aircraft, a neural network classifier is trained on Diamond DA-20 flight data and evaluated on Texan T-6 aircraft under zero-shot and fine-tuned transfer learning conditions. We describe a robust ADS-B preprocessing pipeline integrating digital elevation model (DEM) data, a data labeling strategy using unsupervised learning, and a transfer learning approach enabling adaptation across aircraft types with limited labeled data. Our results demonstrate that transfer learning significantly improves classification accuracy for flight phases with limited data, highlighting the potential of ADS-B-based models to support scalable, behavior-aware airspace intelligence across heterogeneous fleets and permissive environments. This research advances FPI capabilities for USAF training analysis and broader operational priorities in autonomy, situational awareness, and data-driven decision support.