We present an automatic algorithm for graph-based road network extraction from remote sensing images. While existing works mostly focus on improving accuracy, we address the problem of the geometric quality of the output graphs. The state-of-the-art methods largely overlook this aspect by generating graphs without strong geometric guarantees, regularity preservation and low-complexity, which, ultimately, reduces their impact in many application scenarios. Our algorithm relies upon foundation models that analyze road networks with pixel-based representations, as well as geometric algorithms and data structures in charge of connecting geometric primitives into planar graphs. This hybrid strategy allows us to strongly enforce the geometric quality of the output graphs while bringing a high level of generalization. We show the potential of our algorithm and its advantages over existing methods on two datasets commonly-used in the field using both the conventional accuracy metrics and new metrics introduced to measure the geometric quality of the output graphs.