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dc.contributor.author Liu, Jiawei en
dc.contributor.author Tang, Hao en
dc.contributor.author Seiple, William en
dc.contributor.author Zhu, Zhigang en
dc.date.accessioned 2022-06-20T21:01:33Z
dc.date.available 2022-06-20T21:01:33Z
dc.date.issued 2022 en
dc.identifier.citation Journal on Technology and Persons with Disabilities 10: 154-170. en
dc.identifier.issn 2330-4219 en
dc.identifier.uri http://hdl.handle.net/10211.3/223472
dc.description 37th Annual Assistive Technology Conference Scientific/Research Proceedings, online 2022 en
dc.description.abstract The storefront accessibility can substantially impact the way people who are blind or visually impaired (BVI) travel in urban environments. Entrance localization is one of the biggest challenges to the BVI people. In addition, improperly designed staircases and obstructive store decorations can create considerable mobility challenges for BVI people, making it more difficult for them to navigate their community hence reducing their desire to travel. Unfortunately, there are few approaches to acquiring this information in advance through computational tools or services. In this paper, we propose a solution to collect large scale accessibility data of New York City (NYC) storefronts using a crowd-sourcing approach on Google Street View (GSV) panoramas. We develop a web-based crowdsourcing application, DoorFront, which enables volunteers not only to remotely label storefront accessibility data on GSV images, but also to validate the labeling result to ensure high data quality. In order to study the usability and user experience of our application, an informal beta-test is conducted and a user experience survey is designed for testing volunteers. The user feedback is very positive and indicates the high potential and usability of the proposed application. DoorFront has been successfully launched and can be accessed at: https:doorfront.org. en
dc.format application/pdf en
dc.format.extent 16 pages en
dc.language.iso en en
dc.publisher California State University, Northridge. en
dc.rights Copyright 2022 by the authors and California State University, Northridge en
dc.subject Crowdsourcing en
dc.subject Storefront Accessibility en
dc.subject Independent Travel en
dc.subject Visually Impaired en
dc.subject Open-Source Data en
dc.title Annotating Storefront Accessibility Data Using Crowdsourcing en
dc.type Article en
dc.rights.license Creative Commons Attribution-NoDerivs 4.0 Unported License. en


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