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There is a lack of data on the location, condition, and accessibility ofsidewalks across the world, which not only impacts where and how people travelbut also fundamentally limits interactive mapping tools and urban analytics. Inthis paper, we describe initial work in semi-automatically building a sidewalknetwork topology from satellite imagery using hierarchical multi-scaleattention models, inferring surface materials from street-level images usingactive learning-based semantic segmentation, and assessing sidewalk conditionand accessibility features using Crowd+AI. We close with a call to create adatabase of labeled satellite and streetscape scenes for sidewalks and sidewalkaccessibility issues along with standardized benchmarks.

Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities