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.

This paper introduces an innovative approach to road network generation through the utilization of a multi-modal Large Language Model (LLM). Our model is specifically designed to process aerial images of road layouts and produce detailed, navigable road networks within the input images. The core innovation of our system lies in the unique training methodology employed for the large language model to generate road networks as its output. This approach draws inspiration from the BLIP-2 architecture arXiv:2301.12597, leveraging pre-trained frozen image encoders and large language models to create a versatile multi-modal LLM. Our work also offers an alternative to the reasoning segmentation method proposed in the LISA paper arXiv:2308.00692. By training the large language model with our approach, the necessity for generating binary segmentation masks, as suggested in the LISA paper arXiv:2308.00692, is effectively eliminated. Experimental results underscore the efficacy of our multi-modal LLM in providing precise and valuable navigational guidance. This research represents a significant stride in bolstering autonomous navigation systems, especially in road network scenarios, where accurate guidance is of paramount importance.