A building height dataset across China in 2017 estimated by the spatially-informed approach

As a fundamental aspect of the urban form, building height is a key attribute for reflecting human activities and human-environment interactions in the urban context. However, openly accessible building height maps covering the whole China remain sorely limited, particularly for spatially informed data. Here we developed a 1 km × 1 km resolution building height dataset across China in 2017 using Spatially-informed Gaussian process regression (Si-GPR) and open-access Sentinel-1 data. Building height estimation was performed using the spatially-explicit Gaussian process regression (GPR) in 39 major Chinese cities where the spatially explicit and robust cadastral data are available and the spatially-implicit GPR for the remaining 304 cities, respectively. The cross-validation results indicated that the proposed Si-GPR model overall achieved considerable estimation accuracy (R(2) = 0.81, RMSE = 4.22 m) across the entire country. Because of the implementation of local modelling, the spatially-explicit GPR outperformed (R(2) = 0.89, RMSE = 2.82 m) the spatially-implicit GPR (R(2) = 0.72, RMSE = 6.46 m) for all low-rise, mid-rise, and high-rise buildings. This dataset, with extensive-coverage and high-accuracy, can support further studies on the characteristics, causes, and consequences of urbanization.