  We present a new method to create spatial data using a generative adversarial
network (GAN). Our contribution uses coarse and widely available geospatial
data to create maps of less available features at the finer scale in the built
environment, bypassing their traditional acquisition techniques (e.g. satellite
imagery or land surveying). In the work, we employ land use data and road
networks as input to generate building footprints and conduct experiments in 9
cities around the world. The method, which we implement in a tool we release
openly, enables the translation of one geospatial dataset to another with high
fidelity and morphological accuracy. It may be especially useful in locations
missing detailed and high-resolution data and those that are mapped with
uncertain or heterogeneous quality, such as much of OpenStreetMap. The quality
of the results is influenced by the urban form and scale. In most cases, the
experiments suggest promising performance as the method tends to truthfully
indicate the locations, amount, and shape of buildings. The work has the
potential to support several applications, such as energy, climate, and urban
morphology studies in areas previously lacking required data or inpainting
geospatial data in regions with incomplete data.
