Web-based citizen science often involves the classification of image features by large numbers of minimally trained volunteers, such as the identification of lunar impact craters under the Moon Zoo project. Whilst such approaches facilitate the analysis of large image data sets, the inexperience of users and ambiguity in image content can lead to contamination from false positive identifications. We give an approach, using Linear Poisson Models and image template matching, that can quantify levels of false positive contamination in citizen science Moon Zoo crater annotations. Linear Poisson Models are a form of machine learning which supports predictive error modelling and goodness-of-fits, unlike most alternative machine learning methods. The proposed supervised learning system can reduce the variability in crater counts whilst providing predictive error assessments of estimated quantities of remaining true verses false annotations. In an area of research influenced by human subjectivity, the proposed method provides a level of objectivity through the utilisation of image evidence, guided by candidate crater identifications.

中文翻译：

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从公民科学数据估计火山口注释中的假阳性污染

基于网络的公民科学通常涉及由大量训练有素的志愿者对图像特征进行分类，例如月球动物园项目下月球撞击坑的识别。虽然此类方法有助于分析大型图像数据集，但用户缺乏经验和图像内容不明确可能会导致误报识别造成污染。我们提供了一种使用线性泊松模型和图像模板匹配的方法，可以量化公民科学月球动物园火山口注释中的假阳性污染水平。线性泊松模型是机器学习的一种形式，与大多数替代机器学习方法不同，它支持预测误差建模和拟合优度。所提出的监督学习系统可以减少陨石坑计数的可变性，同时提供对剩余真与假注释的估计数量的预测误差评估。在受人类主观影响的研究领域，所提出的方法通过利用图像证据，在候选陨石坑识别的指导下提供了一定程度的客观性。