Roughly 30.6 million individuals in the US have physical disabilities that affect their ambulatory activities; nearly half of those individuals report using an assistive aid such as a wheelchair, cane, crutches, or walker. Despite comprehensive civil rights legislation for Americans with disabilities, many city streets, sidewalks, and businesses remain inaccessible. The problem is not just that street-level accessibility 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.
This project describes a two-pronged vision: first, to develop scalable data collection methods for acquiring sidewalk accessibility information using a combination of crowdsourcing, computer vision, and online map imagery, and second, to use this new data to design, develop, and evaluate a novel set of navigation and map tools for accessibility. Our overarching goal is to transform the ways in which accessibility information is collected and visualized for every sidewalk, street, and building façade in America.
Project Sidewalk combines remote crowd sourcing + machine learning to map the accessibility of the world. Crowd workers virtually walk through cities labeling inaccessible areas. Collected data is used to generate new, accessibility-aware mapping tools and train machine learning algorithms.
