  To meet the challenges of global urbanization, earth observation information
is greatly needed. The lack of global three-dimensional (3D) urban structure
data has been a major limiting factor in important urban applications such as
population mapping, disaster vulnerability assessment, and climate change
adaptation. Due to limited data availability, remote sensing data haven been
mainly used to characterize 3D urban structure at the city scale. In this
study, we propose a method to map 3D urban structure using freely available
Landsat and global elevation data. Building on an object-based machine learning
approach, the synergy of Landsat and elevation data were used to estimate
building height and volume at 30 m resolution. This method has been tested for
the entire country of England and yielded a root-mean-square error (RMSE) of
1.61 m for building height and an RMSE of 1,142 m\textsuperscript{3} for
building volume. The results in this study represent the first attempt to use
open data for urban structure characterization at the country scale. Our
results demonstrated the utility of these data for large-scale urban studies
and the potential of generating global 3D urban structure data products using a
fusion-based approach.
