Understanding and Reducing Crater Counting Errors in Citizen Science Data and the Need for Standardisation
This work addresses the need for standardization in citizen science data for lunar crater counting, which is incremental as it builds on existing methods to improve reliability.
The paper tackled the problem of contamination and missing data in citizen science crater counts by developing a Linear Poisson Model to estimate and remove contamination, achieving highly repeatable crater counts, though correcting for missing data remained difficult.
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.