Dataset Information

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

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.

SUBMITTER: Green SE

PROVIDER: S-EPMC7023201 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

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

Green Siân E SE Rees Jonathan P JP Stephens Philip A PA Hill Russell A RA Giordano Anthony J AJ

Animals : an open access journal from MDPI 20200114 1

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. ...[more]

PMID: 31947586

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Project description:Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data.

Project description:BackgroundCitizen science approaches, which involve members of the public as active collaborators in scientific research, are increasingly being recognized for their potential benefits in chronic disease prevention. However, understanding the potential applicability, feasibility and impacts of these approaches is necessary if they are to be more widely used. This study aimed to synthesize research that has applied and evaluated citizen science approaches in chronic disease prevention and identify key questions, gaps, and opportunities to inform future work in this field.MethodsWe searched six databases (Scopus, Medline, Embase, PsycInfo, PubMed, and CINAHL) in January 2022 to identify articles on the use of citizen science in prevention. We extracted and synthesized data on key characteristics of citizen science projects, including topics, aims and level of involvement of citizen scientists, as well as methods and findings of evaluations of these projects.ResultsEighty-one articles reported on citizen science across a variety of health issues, predominantly physical activity and/or nutrition. Projects primarily aimed to identify problems from the perspective of community members; generate and prioritize solutions; develop, test or evaluate interventions; or build community capacity. Most projects were small-scale, and few were co-produced with policy or practice stakeholders. While around half of projects included an evaluation component, overall, there was a lack of robust, in-depth evaluations of the processes and impacts of citizen science projects.ConclusionsCitizen science approaches are increasingly being used in chronic disease prevention to identify and prioritize community-focused solutions, mobilize support and advocacy, and empower communities to take action to support their health and wellbeing. However, to realize the potential of this approach more attention needs to be paid to demonstrating the feasibility of using citizen science approaches at scale, and to rigorous evaluation of impacts from using these approaches for the diverse stakeholders involved.

Dataset Information

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

Publications

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

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