TraversRL: Traversable Pedestrian Pathway Generation With Reinforcement Learning

Automatically generating pedestrian pathways from aerialimages requires producing a connected network suitable for routing, notjust detecting where sidewalks appear. Sidewalks and crossings, in con-trast to roads, may be partially occluded, implicitly defined, and exhibitcomplex connectivity patterns. Existing segmentation-based approachesfocus on labeling pixels to infer segments, but often produce disconnectedor fragmentary graphs that are unreliable for navigation. We introduceTraversRL, a vision-conditioned model that iteratively grows a path-way network from an aerial image, simulating a traveler navigating thebuilt environment. TraversRL uses an action space of short and long di-rection–distance segments designed to adapt to complex patterns andspan occlusions, and uses a combination of graph-level and step-wise re-wards to balance overall connectivity with precise edge placement. Acrossthree visual backbones and three intersection datasets, TraversRL sub-stantially improves buffered IoU with the ground-truth graph relative toa state-of-the-art segmentation baseline, and more than doubles metricsof connectivity. Moreover, combining global and local rewards producescleaner graphs with fewer spurious branches while further improvingoverall performance. These results demonstrate that modeling pathwayextraction as a sequential decision process from the perspective of a trav-eler, while optimizing for final graph quality with reinforcement learning,produces significantly more reliable pedestrian networks.