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The increasing urbanization of modern cities presents unique challenges to people who are blind and have low vision (BLV), particularly in terms of independent travel and navigation. One critical issue faced by the BLV community is locating entrances to various locales such as stores, restaurants, and buildings. Our research team aims to address this issue by developing an open-source map of New York City storefronts that is accessible to BLV individuals, using machine learning and crowdsourcing approaches.
However, we have encountered several limitations with Google Street View’s data, such as outdated images and obstructions. To overcome these limitations, we have been designing and implementing a crowdsourcing app to gather more precise storefront accessibility information. Through the app, users can capture photos of storefronts, identify and label their accessibility features such as door types, doorknobs, ramps and stairs, and record the precise location of the entrances, with the aim of building an open-source, accessible map of storefronts. By combining technology, community engagement, and a commitment to inclusivity, our research project seeks to enhance the urban experience for BLV individuals and promote a more accessible cityscape.
