This paper explores the application of Pix2Pix, a Generative Adversarial Network (GAN), to transform abstract map images into realistic ground truth images. The authors address the scarcity of such images, which is crucial for urban planning and autonomous vehicle training. The study involves creating a dataset of paired map and aerial images and using a tailored training regimen to enhance the model's performance. The Pix2Pix model, with its conditional adversarial network structure, is shown to accurately render complex urban features, demonstrating its potential for broad real-world applications. The results highlight the model's robustness and its ability to generate high-fidelity ground truth datasets. However, the model still exhibits limitations, particularly in regions with homogeneous textures or repetitive patterns, where artifacts may occur. Future work could focus on integrating more sophisticated loss functions and training strategies to improve the model's performance and reduce translation artifacts.This paper explores the application of Pix2Pix, a Generative Adversarial Network (GAN), to transform abstract map images into realistic ground truth images. The authors address the scarcity of such images, which is crucial for urban planning and autonomous vehicle training. The study involves creating a dataset of paired map and aerial images and using a tailored training regimen to enhance the model's performance. The Pix2Pix model, with its conditional adversarial network structure, is shown to accurately render complex urban features, demonstrating its potential for broad real-world applications. The results highlight the model's robustness and its ability to generate high-fidelity ground truth datasets. However, the model still exhibits limitations, particularly in regions with homogeneous textures or repetitive patterns, where artifacts may occur. Future work could focus on integrating more sophisticated loss functions and training strategies to improve the model's performance and reduce translation artifacts.