Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities
This addresses the problem of limited urban mobility data for people with disabilities and urban planners, though it appears incremental as it describes initial work.
The paper tackles the global lack of sidewalk accessibility data by developing semi-automatic methods to map sidewalk networks from satellite imagery and assess conditions from street-level images using Crowd+AI techniques, resulting in initial work toward creating a labeled database and standardized benchmarks.
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.