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Large-scale analysis of pedestrian infrastructures, particularly sidewalks,is critical to human-centric urban planning and design. Benefiting from therich data set of planimetric features and high-resolution orthoimages providedthrough the New York City Open Data portal, we train a computer vision model todetect sidewalks, roads, and buildings from remote-sensing imagery and achieve83% mIoU over held-out test set. We apply shape analysis techniques to studydifferent attributes of the extracted sidewalks. More specifically, we do atile-wise analysis of the width, angle, and curvature of sidewalks, which asidefrom their general impacts on walkability and accessibility of urban areas, areknown to have significant roles in the mobility of wheelchair users. Thepreliminary results are promising, glimpsing the potential of the proposedapproach to be adopted in different cities, enabling researchers andpractitioners to have a more vivid picture of the pedestrian realm.

Sidewalk Measurements from Satellite Images: Preliminary Findings