Assessing quality of volunteer crowdsourcing contributions: lessons from the Cropland Capture game

Volunteered Geographical Information (VGI) is the assembly of spatial information based on public input. While VGI has proliferated in recent years, assessing the quality of volunteercontributed data has proven challenging, leading some to question the efficiency of such programs. In this paper, we compare several quality metrics for individual volunteers’ contributions. The data was the product of the ‘Cropland Capture’ game, in which several thousand volunteers assessed 165,000 images for the presence of cropland over the course of six months. We compared agreement between volunteer ratings and an image’s majority classification with volunteer self-agreement on repeated images and expert evaluations. We also examined the impact of experience and learning on performance. Volunteer selfagreement was nearly always higher than agreement with majority classifications, and much greater than agreement with expert validations, although these metrics were all positively correlated. Volunteer quality showed a broad trend toward improvement with experience, but the highest accuracies were achieved by a handful of moderately active contributors, not the most active volunteers. Our results emphasize the importance of a universal set of expertvalidated tasks as a gold standard for evaluating VGI quality.