Accurately measuring street dimensions is essential to evaluating how their design influences both travel behavior and safety. However, gathering street-level information at city-scale with precision is difficult given the quantity and complexity of urban intersections. To address this challenge in the context of pedestrian crossings — a crucial component of walkability — we introduce a scalable and accurate method for automatically measuring crossing distance at both marked and unmarked crosswalks, applied to America’s 100 largest cities. First, OpenStreetMap coordinates were used to retrieve satellite imagery of intersections throughout each city — totaling roughly three million images. Next, Meta’s Segment Anything Model was trained on a manually labelled subset of these images to differentiate drivable from non-drivable surfaces (i.e., roads vs. sidewalks). Third, all available crossing edges from OpenStreetMap were extracted. Finally, crossing edges were overlaid on the segmented intersection images, and a grow-cut algorithm was applied to connect each edge to its adjacent non-drivable surface (e.g., sidewalk, private property, etc.), thus enabling the calculation of crossing distance. This achieved 93% accuracy in measuring crossing distance, with a median absolute error of 2 feet 3 inches (0.69 meters), when compared to manually verified data for an entire city. Across the 100 largest U.S. cities, median crossing distances ranged from 32 feet to 78 feet (9.8 – 23.8m), with detectable regional patterns. Median crossing distance also displayed a positive relationship with the cities’ year of incorporation, illustrating in a novel way how American city planning increasingly emphasizes wider (and more car-centric) streets. These findings identified opportunities to improve pedestrian safety and increase walkability at multiple scales, from the individual block to the entire city.
Moran et al. (2026) studied this question.