GANmapper: geographical content filling. (arXiv:2108.04232v1 [cs.CV])

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 generating approximate maps of the urban form, and it is
generalisable to augment other types of geoinformation, enhancing the
completeness and quality of spatial data infrastructure. 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, 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



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