  Generative Adversarial Networks (GANs) have significantly advanced image
processing, with Pix2Pix being a notable framework for image-to-image
translation. This paper explores a novel application of Pix2Pix to transform
abstract map images into realistic ground truth images, addressing the scarcity
of such images crucial for domains like urban planning and autonomous vehicle
training. We detail the Pix2Pix model's utilization for generating
high-fidelity datasets, supported by a dataset of paired map and aerial images,
and enhanced by a tailored training regimen. The results demonstrate the
model's capability to accurately render complex urban features, establishing
its efficacy and potential for broad real-world applications.
