Citizen science, computing, and conservation: How can "Crowd AI" change the way we tackle large-scale ecological challenges?

Camera traps - remote cameras that capture images of passing wildlife - have become a ubiquitous tool in ecology and conservation. Systematic camera trap surveys generate ‘Big Data’ across broad spatial and temporal scales, providing valuable information on environmental and anthropogenic factors affecting vulnerable wildlife populations. However, the sheer number of images amassed can quickly outpace researchers’ ability to manually extract data from these images (e.g., species identities, counts, and behaviors) in timeframes useful for making scientifically-guided conservation and management decisions. Here, we present ‘Snapshot Safari’ as a case study for merging citizen science and machine learning to rapidly generate highly accurate ecological Big Data from camera trap surveys. Snapshot Safari is a collaborative cross-continental research and conservation effort with 1500+ cameras deployed at over 40 eastern and southern Africa protected areas, generating millions of images per year. As one of the first and largest-scale camera trapping initiatives, Snapshot Safari spearheaded innovative developments in citizen science and machine learning. We highlight the advances made and discuss the issues that arose using each of these methods to annotate camera trap data. We end by describing how we combined human and machine classification methods (‘Crowd AI’) to create an efficient integrated data pipeline. Ultimately, by using a feedback loop in which humans validate machine learning predictions and machine learning algorithms are iteratively retrained on new human classifications, we can capitalize on the strengths of both methods of classification while mitigating the weaknesses. Using Crowd AI to quickly and accurately ‘unlock’ ecological Big Data for use in science and conservation is revolutionizing the way we take on critical environmental issues in the Anthropocene era.