Artificial Intelligence Meets Citizen Science to Supercharge Ecological Monitoring

Patterns (N Y). 2020 Oct 9;1(7):100109. doi: 10.1016/j.patter.2020.100109.


The development and uptake of citizen science and artificial intelligence (AI) techniques for ecological monitoring is increasing rapidly. Citizen science and AI allow scientists to create and process larger volumes of data than possible with conventional methods. However, managers of large ecological monitoring projects have little guidance on whether citizen science, AI, or both, best suit their resource capacity and objectives. To highlight the benefits of integrating the two techniques and guide future implementation by managers, we explore the opportunities, challenges, and complementarities of using citizen science and AI for ecological monitoring. We identify project attributes to consider when implementing these techniques and suggest that financial resources, engagement, participant training, technical expertise, and subject charisma and identification are important project considerations. Ultimately, we highlight that integration can supercharge outcomes for ecological monitoring, enhancing cost-efficiency, accuracy, and multi-sector engagement.

Keywords: AI; CS; automation; big data; biological conservation; data processing; deep learning; machine learning.

Publication types

  • Review