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Estimating False Positive Contamination in Crater Annotations from Citizen Science Data.

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Area of Science:

  • Planetary Science
  • Astronomy
  • Citizen Science

Background:

  • Citizen science projects, such as Moon Zoo, leverage public participation for image analysis, like identifying lunar impact craters.
  • Volunteer inexperience and image ambiguity can introduce false positive identifications, compromising data quality.

Purpose of the Study:

  • To develop and present a novel method for quantifying false positive contamination in citizen science image annotations.
  • To improve the accuracy and reliability of data derived from large-scale citizen science initiatives.

Main Methods:

  • Utilized Linear Poisson Models, a machine learning technique supporting predictive error modeling.
  • Implemented image template matching for candidate crater identification.
  • Developed a supervised learning system to assess and reduce annotation variability.

Main Results:

  • The proposed method successfully quantifies levels of false positive contamination in Moon Zoo crater annotations.
  • The system demonstrated a reduction in variability of crater counts.
  • Provided predictive error assessments for distinguishing true from false annotations.

Conclusions:

  • The developed approach offers an objective, evidence-based method for validating citizen science data.
  • Enhances the reliability of scientific findings derived from large, crowdsourced image datasets.
  • Addresses human subjectivity in image analysis through a quantitative machine learning framework.