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