Integrating Advanced Visualization and Automated HPC Workflows for GNSS Prediction in Urban Air Mobility

Urban Air Mobility (UAM) brings forth new complex challenges, necessitating the development of advanced technologies to meet the specialized requirements of this emerging sector. Predictive and proactive risk mitigation capabilities will likely be necessary to ensure that these novel aviation operations are safely integrated into the National Airspace System (NAS). This is especially true when considering the operational complexity and low risk tolerance associated with highly autonomous vehicles in urban environments. By analyzing potential hazards and identifying where and when high-risk scenarios might arise, flight planning tools can be leveraged to help reduce exposure to such risks. Currently, UAM systems rely heavily on Global Navigation Satellite Systems (GNSS) for accurate positioning. However, one significant safety concern is the loss or degradation of this critical navigation system. Particularly in low-altitude flights and urban settings, obstructions like buildings and trees are common and can cause reduced satellite visibility. Accurately predicting satellite visibility at varying altitudes and times is therefore essential to enhance the safety of UAM operations. This paper introduces an automated High Performance Computing (HPC) workflow backend and a Graphical User Interface (GUI) frontend to support flight planning by producing advanced visualizations to analyze the risk of GNSS performance degradation or loss caused by obstructed satellite visibility. The backend and frontend presented in this paper are designed for NavQ, a GNSS quality prediction service. NavQ allows mission planners to identify safe flight trajectories and operational zones within urban landscapes to minimize possible failure caused by poor navigation data.