Generative Adversarial Network (GAN) is widely used in many generative problems, including in spatial information sciences and urban systems. The data generated by GANs can achieve high quality to augment downstream training or to complete missing entries in a dataset. GANs can also be used to learn the relationship between two datasets and translate one into another, e.g. road network data into building footprint data. However, such approach has not been developed in the geospatial and urban...
