  Automatically converting text descriptions into images using transformer
architectures has recently received considerable attention. Such advances have
implications for many applied design disciplines across fashion, art,
architecture, urban planning, landscape design and the future tools available
to such disciplines. However, a detailed analysis capturing the capabilities of
such models, specifically with a focus on the built environment, has not been
performed to date. In this work, we investigate the capabilities and biases of
such text-to-image methods as it applies to the built environment in detail. We
use a systematic grammar to generate queries related to the built environment
and evaluate resulting generated images. We generate 1020 different images and
find that text to image transformers are robust at generating realistic images
across different domains for this use-case. Generated imagery can be found at
the github: https://github.com/sachith500/DALLEURBAN
