Accurate and up-to-date road maps are vital for Automated Vehicles (AVs) to navigate urban environments safely and predict the behavior of surrounding agents. However, generating such maps typically requires manual annotation or depends on expensive sensor-equipped vehicles, limiting scalability. Aerial imagery offers a more scalable alternative, but existing methods either fail to capture both geometric and topological details or do not generalize well to unseen urban areas.
This thesis addresses these challenges by introducing SAM-Maps, a novel framework that automatically generates road maps from aerial imagery without requiring additional model training. Leveraging foundation models, SAM-Maps extracts both the drivable area geometry and road connectivity of urban environments.
Experiments on the View-of-Delft Prediction dataset demonstrate that SAM-Maps achieves a recall of 43.4% in a fully automatic mode, improving to 75.6% with minimal manual steps. Additionally, trajectory prediction experiments using the state-of-the-art Wayformer model show a 37.9% improvement on the minADE6 metric when incorporating SAM-Maps, compared to scenarios without map input. To the best of our knowledge, this is the first method to extract both drivable areas and road connections for European urban environments from aerial imagery, providing a scalable solution for road map generation.