Understanding and Reducing Crater Counting Errors in Citizen Science Data and the Need for Standardisation

TL;DR

This paper introduces a quantitative method using Linear Poisson Models to assess and correct contamination in citizen science lunar crater data, highlighting the importance of standardization for reliable crater counting.

Contribution

It presents a novel pattern recognition approach to quantify and reduce contamination effects in citizen science crater datasets, improving data reliability.

Findings

Contamination can be effectively estimated and removed from crater data.

Crater counts become highly repeatable after correction for contamination.

Correcting for missing data remains challenging.

Abstract

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.