Characterizing and Visualizing Physical World Accessibility at Scale Using Crowdsourcing, Computer Vision, and Machine Learning
Kotaro Hara and Jon E. Froehlich
Poorly maintained sidewalks and street intersections pose considerable accessibility challenges for people with mobility-impairments. According to the most recent U.S. Census (2010), roughly 30.6 million adults have physical disabilities that affect their ambulatory activities. Of these, nearly half report using an assistive aid such as a wheelchair (3.6 million) or a cane, crutches, or walker (11.6 million). Despite comprehensive civil rights legislation for Americans with Disabilities, many city streets, sidewalks, and businesses in the U.S. remain inaccessible. The problem is not just that street-level accessibility fundamentally affects where and how people travel in cities, but also that there are few, if any, mechanisms to determine accessible areas of a city a priori. Indeed, in a recent report, the National Council on Disability noted that they could not find comprehensive information on the “degree to which sidewalks are accessible” across the US. This lack of information can have a significant negative impact on the independence and mobility of citizens. For example, in our own initial formative interviews with wheelchair users, we uncovered a prevailing view about navigating to new areas of a city: “I usually don’t go where I don’t know [about accessible routes]” (Interviewee 3, congenital polyneuropathy). Our overarching research vision is to transform the way in which street-level accessibility information is collected and used to support new types of assistive map-based tools...