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.

翻译：生成对抗网络（GANs）显著推动了图像处理技术的发展，其中Pix2Pix作为图像到图像翻译的典型框架，本文探究了其在新应用场景中的潜力——将抽象地图图像转化为逼真的地面真值图像，以解决城市规划与自动驾驶车辆训练等关键领域对此类图像稀缺的困境。我们详细阐述了Pix2Pix模型在生成高保真数据集中的应用，该模型基于配对的卫星地图与航拍图像数据集，并辅以定制化训练策略。实验结果表明，该模型能够精确呈现复杂的城市特征，验证了其有效性及在广泛实际场景中的应用潜力。