A Risk Assessment Method of Three-Dimensional Low-Attitude Airspace Based on Multi-Source Data

The safe operation of low-altitude UAVs is crucial for the effective utilization of low-altitude airspace, necessitating the development of appropriate risk assessment methods to evaluate the associated operational risks. However, current research primarily focuses on two-dimensional risk assessments, with limited focus on assessing risks across different heights, thus constraining the ability to guide UAV operations within three-dimensional airspace. In this study, we propose a three-dimensional airspace risk assessment method that integrates multisource data to estimate risks at various altitudes. First, we assess ground impact risks by considering factors such as population density, obstacle environment, and socioeconomic characteristics. Next, we develop a network signal evaluation model to estimate signal loss at various altitudes. Finally, we apply machine learning methods to classify multiple features to determine airspace risks at varying altitudes, resulting in a comprehensive three-dimensional risk map. The results indicate that the majority of the urban area falls within the low-risk category, accounting for approximately 84–87% of the city. High-risk regions are concentrated in central urban areas, with their proportion increasing from 5.9% at 30 m to 9.1% at 300 m. Although the overall trend remains broadly consistent across altitudes, the local variations highlight the necessity of three-dimensional risk evaluation. This three-dimensional risk map can effectively guide safe UAV operations across different altitude layers and provide valuable decision support for flight route planning.