arXiv Machine Learning September 17, 2026 By Seung Jae Lieu, Diego Morra, Chiara Cadoni, Wonseop Song, Martina Mazzarello, Carlo Ratti

Can VLMs Reliably Assess Sidewalk Accessibility Attributes from Pedestrian-Level Imagery?

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The study evaluates whether vision‑language models (VLMs) can reliably assess sidewalk accessibility attributes—effective width, longitudinal slope, cross slope, and pavement condition—from pedestrian‑level images. Using sampling‑based conformal prediction on 514 images from Seoul, the authors find that calibrated models achieve nominal 90% coverage, but only effective width yields informative estimates; other attributes remain too uncertain for compliance assessment. The work also demonstrates that raw sampling dispersion is not a trustworthy uncertainty measure without calibration and releases annotated images with ground‑truth measurements.

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