GLOBE Observer: A Case Study in Advancing Earth System Knowledge with AI-Powered Citizen Science - Peder V. Nelson; Russanne Low; Holli Kohl; David Overoye; Di Yang; Xiao Huang; Sriram Chellappan; Farhat Binte Azam; Ryan M. Carney; Monika Falk; Joan Garriga; Larisa Schelkin; et al. (Citizen Science: Theory and Practice)
Citizen science and artificial intelligence (AI) complement each other by harnessing the strengths of both human and machine capabilities. Citizen science generates terabytes of raw numerical, text, and image data, the analysis of which requires automated techniques to process in an efficient manner. Conversely, AI computer vision technology can require tens of thousands of images during the training process, and citizen science projects are well suited to provide large libraries of data. Herein, we describe how AI tools are being applied across the GLOBE Observer citizen science data ecosystem, where image recognition algorithms are supporting data ingest processes, protecting user privacy and improving data fidelity. GLOBE citizen science data has been used to develop automated data classification routines that enable information discovery of mosquito larvae and land cover labels. These advances position GLOBE citizen scientist data for discovery and use in environmental and health research, as well as by machine learning scientists working in the general field of GeoAI.