Shijian Gao

Radio maps provide the essential foundation for low-altitude networking systems. Unlike terrestrial radio maps that are typically generated via drive test measurements, mapping the air-ground environment requires the deployment of unmanned aerial vehicles (UAVs). This shift introduces two formidable challenges in uncharted 3D scenarios. First, sparse radio measurements and incomplete geometric observations hinder accurate reconstruction. Second, the large 3D action space and strict power constraints from high spectrum scanner energy consumption make informative exploration difficult. To address these issues, we propose 3D uncertainty aware radio active mapping (3D-URAM), a closed loop active perception framework that decouples the mapping process into two offline trained stages. In Stage I, a Bayesian UNet is developed to recover radio maps from sparse measurements and partial geometry while providing calibrated predictive uncertainty. In Stage II, a dynamic probabilistic roadmap and a transformer based waypoint selection policy trained via proximal policy optimization maximize long horizon uncertainty reduction under travel budgets. Experimental results demonstrate that 3D-URAM reduces reconstruction error by over 50% compared to representative baselines. Real-world field tests within a 300m×200m×100m space also validate the potential of active radio map reconstruction.