Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2013

Abstract

Jon FroehlichAbstractPoorly maintained sidewalks, missing curb ramps, and other obstacles pose considerable accessibility challenges; however, there are currently few, if any, mechanisms to determine accessible areas of a city a priori. In this paper, we investigate the feasibility of using untrained crowd workers from Amazon Mechanical Turk (turkers) to find, label, and assess sidewalk accessibility problems in Google Street View imagery. We report on two studies: Study 1 examines the feasibility of this labeling task with six dedicated labelers including three wheelchair users; Study 2 investigates the comparative performance of turkers. In all, we collected 13,379 labels and 19,189 verification labels from a total of 402 turkers. We show that turkers are capable of determining the presence of an accessibility problem with 81% accuracy. With simple quality control methods, this number increases to 93%. Our work demonstrates a promising new, highly scalable method for acquiring knowledge about sidewalk accessibility.

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

CHI '13 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Paris, France, 2013, April 27 - May 02

First Page

631

Last Page

640

ISBN

978-1-4503-1899-0

Identifier

10.1145/2470654.2470744

Publisher

ACM New York

City or Country

Paris, France

Additional URL

https://doi.org/10.1145/2470654.2470744

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