  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.
