Using Machine Learning to Reduce Observational Biases When Detecting New Impacts on Mars. (arXiv:2207.05679v1 [cs.LG])

The current inventory of recent (fresh) impacts on Mars shows a strong bias
towards areas of low thermal inertia. These areas are generally visually
bright, and impacts create dark scours and rays that make them easier to
detect. It is expected that impacts occur at a similar rate in areas of higher
thermal inertia, but those impacts are under-detected. This study investigates
the use of a trained machine learning classifier to increase the detection of
fresh impacts on Mars using …

arxiv biases impacts learning lg machine machine learning mars reduce