Low-level air traffic modelling for unmanned aircraft integration

This paper presents a new data-driven air traffic modelling and analysis technique that can support operational risk analysis for unmanned aircraft integration. The proposed technique exploits advances in computer vision to autonomously extract and analyse the spatial distribution of arbitrary traffic densities, which can provide the foundation for quantitative and tailored risk assessments. The framework can manage large volumes of air traffic data at very low computational cost, and can be customised for other traffic analysis tasks. This unique approach represents a more natural way to process and visualise air traffic data for use by unmanned aircraft operators, regulators and air navigation service providers considering future airspace environments