Cognition and Modeling of Urban Low-Altitude Airspace

The rapid advancement of UAV technology has highlighted the importance of managing low-altitude airspace and planning efficient, safe flight paths in complex urban environments, where refined airspace modeling and connectivity analysis are essential for effective UAV operations.In this study, we propose a low-altitude airspace modeling and visibility analysis method based on the Alpha-Shape algorithm and space syntax theory to address the challenge of UAV airspace cognition in complex urban environments. Utilizing the Alpha-Shape algorithm, we construct the reachable urban airspace that encompasses buildings and obstacles, adapting to various flight requirements by adjusting algorithm parameters. We then generate visibility graphs through isovist analysis to quantitatively evaluate the spatial connectivity of different regions within the airspace. Case studies of complex urban areas demonstrate that densely built environments significantly obstruct UAV flights, while zones with open views exhibit superior connectivity, making them ideal for UAV path planning. This method is shown to be feasible to UAV path planning and can be further extended to related fields such as low-altitude route design, critical hub node identification and takeoff and landing site selection.