  Recent widespread applications for unmanned aerial vehicles (UAVs) -- from
infrastructure inspection to urban logistics -- have prompted an urgent need
for high-accuracy three-dimensional (3D) radio maps. However, existing methods
designed for two-dimensional radio maps face challenges of high measurement
costs and limited data availability when extended to 3D scenarios. To tackle
these challenges, we first build a real-world large-scale 3D radio map dataset,
covering over 4.2 million m^3 and over 4 thousand data points in complex urban
environments. We propose a Gaussian Process Regression-based scheme for 3D
radio map estimation, allowing us to realize more accurate map recovery with a
lower RMSE than state-of-the-art schemes by over 2.5 dB. To further enhance
data efficiency, we propose two methods for training point selection, including
an offline clustering-based method and an online maximum a posterior
(MAP)-based method. Extensive experiments demonstrate that the proposed scheme
not only achieves full-map recovery with only 2% of UAV measurements, but also
sheds light on future studies on 3D radio maps.
