  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.
