Novel automated classification approaches for citizen science - Jiménez Morales, Manuel Alejandro (2020)
Citizen science, traditionally known as the engagement of amateur participants in research, is showing a great potential for large-scale processing of data. In areas such as astronomy, ecology, or geo-sciences, where emerging technologies generate huge volumes of data, citizen science projects enable image classification at a rate not possible to accomplish by experts alone. Using the power of the web, virtual communities of volunteers sharing a common goal are able to coordinate the classification of hundreds of thousands of images in a reasonable amount of time. However, expert evaluations usually reveal biases and uncertainty in the results, since the participants involved are typically inexperienced in the task and hold variable skills and backgrounds. Consequently, the research community tends to distrust citizen science outcomes, claiming a generalised lack of accuracy and validation, and leaving the major part of the resulting data unemployed after the finalisation of the projects.