论文标题
理解和减少公民科学数据中的陨石坑计数错误以及对标准化的需求
Understanding and Reducing Crater Counting Errors in Citizen Science Data and the Need for Standardisation
论文作者
论文摘要
公民科学已成为初步数据处理任务的流行工具，例如识别和计算现代高分辨率图像中的月球影响陨石坑。但是，使用此类数据要求公民科学产品是可以理解和可靠的。污染和缺少数据可以降低数据集的实用性，因此必须量化此类效果很重要。本文介绍了一种基于新开发的定量模式识别系统（线性泊松模型），用于估计Moonzoo Citizen Science Crater数据中的污染水平。证据将表明，有可能在某些商定的地面真理中消除污染的影响，从而导致估计的火山口计数高度可重复。但是，还将表明，目前难以实现丢失数据的纠正。这些技术在Apollo 17站点的Moonzoo公民科学火山口注释中进行了测试，并在同一地区的本科生和专家成绩进行了测试。
Citizen science has become a popular tool for preliminary data processing tasks, such as identifying and counting Lunar impact craters in modern high-resolution imagery. However, use of such data requires that citizen science products are understandable and reliable. Contamination and missing data can reduce the usefulness of datasets so it is important that such effects are quantified. This paper presents a method, based upon a newly developed quantitative pattern recognition system (Linear Poisson Models) for estimating levels of contamination within MoonZoo citizen science crater data. Evidence will show that it is possible to remove the effects of contamination, with reference to some agreed upon ground truth, resulting in estimated crater counts which are highly repeatable. However, it will also be shown that correcting for missing data is currently more difficult to achieve. The techniques are tested on MoonZoo citizen science crater annotations from the Apollo 17 site and also undergraduate and expert results from the same region.
