Wildwatch Kenya expert verified data

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 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.