Automated 3D city generation, focusing on road networks and building layouts, is in high demand for applications in urban design, multimedia games and autonomous driving simulations. The surge of generative AI facilitates designing city layouts based on deep learning models. However, the lack of high-quality datasets and benchmarks hinders the progress of these data-driven methods in generating road networks and building layouts. Furthermore, few studies consider urban characteristics, which generally take graphics as analysis objects and are crucial for practical applications, to control the generative process. To alleviate these problems, we introduce a multimodal dataset with accompanying evaluation metrics for controllable generation of Road networks and Building layouts (RoBus), which is the first and largest open-source dataset in city generation so far. RoBus dataset is formatted as images, graphics and texts, with $72,400$ paired samples that cover around $80,000km^2$ globally. We analyze the RoBus dataset statistically and validate the effectiveness against existing road networks and building layouts generation methods. Additionally, we design new baselines that incorporate urban characteristics, such as road orientation and building density, in the process of generating road networks and building layouts using the RoBus dataset, enhancing the practicality of automated urban design. The RoBus dataset and related codes are published at https://github.com/tourlics/RoBus_Dataset.

翻译：面向道路网络与建筑布局的自动化三维城市生成技术，在城市设计、多媒体游戏及自动驾驶仿真等领域具有迫切需求。生成式人工智能的兴起推动了基于深度学习模型的城市布局设计。然而，高质量数据集与基准测试的缺失，制约了数据驱动方法在道路网络与建筑布局生成方面的发展。此外，现有研究鲜少考虑以图形为分析对象的城市特征——这类特征对实际应用至关重要——来调控生成过程。为缓解上述问题，我们提出了用于道路网络与建筑布局可控生成的多模态数据集及配套评估指标（RoBus），这是目前城市生成领域首个且规模最大的开源数据集。RoBus数据集以图像、图形和文本形式构建，包含$72,400$组配对样本，覆盖全球约$80,000km^2$区域。我们对RoBus数据集进行了统计分析，并验证了其在现有道路网络与建筑布局生成方法上的有效性。此外，基于RoBus数据集，我们设计了融合道路走向、建筑密度等城市特征的新型基线模型，用于道路网络与建筑布局的生成过程，从而提升了自动化城市设计的实用性。RoBus数据集及相关代码已发布于https://github.com/tourlics/RoBus_Dataset。