Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence

Green, S.E.; Rees, J.P.; Stephens, P.A.; Hill, R.A.; Giordano, A.J.

Abstract

Camera trapping has become an increasingly reliable and mainstream tool for surveying a diversity of wildlife species. Concurrent with this has been an increasing effort to involve the wider public in the research process, in an approach known as ‘citizen science’. To date, millions of people have contributed to research across a wide variety of disciplines as a result. Although their value for public engagement was recognised early on, camera traps were initially ill‐suited for citizen science. As camera trap technology has evolved, cameras have become more user‐friendly and the enormous quantities of data they now collect has led researchers to seek assistance in classifying footage. This has now made camera trap research a prime candidate for citizen science, as reflected by the large number of camera trap projects now integrating public participation. Researchers are also turning to Artificial Intelligence (AI) to assist with classification of footage. Although this rapidly‐advancing field is already proving a useful tool, accuracy is variable and AI does not provide the social and engagement benefits associated with citizen science approaches. We propose, as a solution, more efforts to combine citizen science with AI to improve classification accuracy and efficiency while maintaining public involvement.

Citation

Green, S., Rees, J., Stephens, P., Hill, R., & Giordano, A. (2020). Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence. Animals, 10(1), Article 132. https://doi.org/10.3390/ani10010132

• Journal Article Type: Article
• Acceptance Date: Jan 10, 2020
• Online Publication Date: Jan 14, 2020
• Publication Date: Jan 14, 2020
• Deposit Date: Jan 10, 2020
• Publicly Available Date: Jan 14, 2020
• Journal: Animals
• Electronic ISSN: 2076-2615
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 1
• Article Number: 132
• DOI: https://doi.org/10.3390/ani10010132
• Public URL: https://durham-repository.worktribe.com/output/1279940