  Generative Adversarial Networks (GANs) are a well-known technique that is
trained on samples (e.g. pictures of fruits) and which after training is able
to generate realistic new samples. Conditional GANs (CGANs) additionally
provide label information for subclasses (e.g. apple, orange, pear) which
enables the GAN to learn more easily and increase the quality of its output
samples. We use GANs to learn architectural features of major cities and to
generate images of buildings which do not exist. We show that currently
available GAN and CGAN architectures are unsuited for this task and propose a
custom architecture and demonstrate that our architecture has superior
performance for this task and verify its capabilities with extensive
experiments.
