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Camera settings and biome influence the accuracy of citizen science approaches to camera trap image classification

机译：相机设置和生物群系会影响公民科学方法对相机陷阱图像分类的准确性

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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 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.
机译：摘要科学家们正在越来越多地使用公民科学家通过运动激活线索相机拍摄的图像分类的志愿工作。公民科学的日益普及，反映其潜在从事保护科学公共和加快大容量的线索相机生成图像的处理。虽然通过公民科学家图像分类准确度也会因种类而异，其他因素对精度的影响知之甚少。减少不准确公民科学得出的数据和提示需要针对具体的最佳实践协议来减少误差值。我们比较三个方案使用众包公民科学家网上处理图像的精度：快照塞伦盖蒂，Wildwatch肯尼亚和AmazonCam坦博帕塔。我们假设，生境类型和相机设置会影响精度。为了评估这些因素，每张照片分发给多个志愿者。所有志愿者的分类已于聚合以用于使用多个算法每张照片的单个最佳答案。随后，这些图像的一个子集进行专家评审和比较，公民科学家结果。分类错误是由错误（例如，错误的种类或假空）的性质，以及原因假分类（例如，误认）分类。我们的研究结果表明，快照塞伦盖蒂具有最高的准确度（97.9％），其次是AmazonCam坦博帕塔（93.5％），然后Wildwatch肯尼亚（83.4％）。错误类型由栖息地的影响，与假空图像相比林地（10％）的开放式草地栖息地（27％）更普遍。对于媒体在所有生境类型大型动物的调查，我们的研究结果表明，以显著提升在众包项目的准确性，研究人员应该使用一个线索相机设置了协议与突发的连续三张照片，视短场，并确定摄像机的灵敏度在原位测试基础上的设置。精度等级比较像这样的研究可以提高未来公民科学项目的可靠性，随后鼓励增加使用这些数据。

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