Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence
Problem
Camera traps now collect enormous quantities of footage that researchers struggle to classify, and the paper reviews how citizen science and AI are being integrated to handle this.
Why it matters
Wildlife surveying at scale depends on classifying millions of images, which is beyond manual research capacity.
What the AI did
The paper reviews researchers turning to Artificial Intelligence to assist with classification of camera trap footage, alongside public participation.
Results
not stated in source
Impact
The evidence only supports that AI-assisted classification is an emerging, rapidly-advancing approach in camera trap research.
Record
| Venue | Animals |
|---|---|
| Published | 2020 |
| Work type | article |
| Authors | Siân E. Green, Jonathan P. Rees, Philip A. Stephens, Russell A. Hill, Anthony J. Giordano |
| Citations | 110 (OpenAlex, at import time) |
| References | 89 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/ani10010132 |
| Open access | gold |
| Keywords | camera trapping, citizen science, artificial intelligence, wildlife monitoring, image classification |
Importance rating
real but narrower advance
Camera traps collect enormous data quantities beyond manual capacity; AI-assisted classification is emerging but accuracy variable; review proposes combining with citizen science.