Camera settings and habitat type influence the accuracy of citizen
science approaches to camera trap image classification.
Abstract
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 are 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 photo was circulated to multiple
volunteers. All volunteer classifications were aggregated to a single
best answer for each photo 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 photos, a short field of view, and consider appropriate
camera sensitivity. Accuracy level comparisons such as this study can
improve reliability of future citizen science projects, and subsequently
encourage the increased use of such data.