Increasing the reliability of citizen science campaign data for deforestation detection in tropical forests

Abstract

In recent years, citizen science (CS) campaigns leveraging crowdsourcing have proven effective in generating large datasets across various fields such as environmental monitoring, and astronomy. However, the quality of volunteer-contributed data remains a challenge, as inconsistent responses often arise from inattentiveness and rapid analyses. To increase reliability in the generation of labeled datasets in citizen science campaigns, this paper proposes the combination of outlier detection techniques (Z-Score, Tukey and Median Absolute Deviation) to remove unreliable voluntary contributions, followed by exclusion of tasks with high Shannon entropy, that is, without consensus of volunteers. To validate this methodology, a case study was conducted using three CS campaigns from the ForestEyes project, which employs citizen science and machine learning to detect deforested areas. The results showed that applying those statistical techniques to filter contributions based on response time of the volunteers joining with median entropy filter led to a growth of up to 20 % of accuracy in campaigns, highlighting the importance of integrating statistical techniques and variability to improve the CS results.