Handling uncertainty in citizen science data: Towards an improved amateur-based large-scale classification

Abstract

Citizen Science, traditionally known as the engagement of amateur participants in research, is showing great potential for large-scale processing of data. In areas such as astronomy, biology, 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. However, this approach entails the spread of biases and uncertainty in the results, since participants involved are typically non-experts in the problem and hold variable skills. Consequently, the research community tends not to trust Citizen Science outcomes, claiming a generalised lack of accuracy and validation. We introduce a novel multi-stage approach to handle uncertainty within data labelled by amateurs in Citizen Science projects. Firstly, our method proposes a set of transformations that leverage the uncertainty in amateur classifications. Then, a hybridisation strategy provides the best aggregation of the transformed data for improving the quality and confidence in the results. As a case study, we consider the Galaxy Zoo, a project pursuing the labelling of galaxy images. A limited set of expert classifications allow us to validate the experiments, confirming that our approach is able to greatly boost accuracy and classify more images with respect to the state-of-art.