Combining crowdsourcing and google street view to identify street-level accessibility problems

Authors:
Kotaro Hara;Vicki Le;Jon Froehlich
Affiliations:
University of Maryland, College Park, Maryland, USA;University of Maryland, College Park, Maryland, USA;University of Maryland, College Park, Maryland, USA
Venue:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Year:
2013

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Abstract

Poorly 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.