Authors

Kotaro Hara, Victoria Le, and Jon Froehlich

Publication

ASSETS '12: Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility, October 2012, Pages 273–274, https://doi.org/10.1145/2384916.2384989

Abstract

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.

Paper

A feasibility study of crowdsourcing and google street view to determine sidewalk accessibility

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.

dl.acm.org