Combining crowdsourcing and google street view to identify street-level accessibility problems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility
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We explore the feasibility of using crowd workers from Amazon Mechanical Turk to identify and rank sidewalk accessibility issues from a manually curated database of 100 Google Street View images. We examine the effect of three different interactive labeling interfaces (Point, Rectangle, and Outline) on task accuracy and duration. We close the paper by discussing limitations and opportunities for future work.