# Using Machine Learning to Reduce Observational Biases When Detecting New Impacts on Mars

arXiv: https://arxiv.org/abs/2207.05679 · HTML: https://mlsanity.com/paper/2207.05679/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 12 Jul 2022.

## Builds on (most cited first, 4 of 4)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances](https://mlsanity.com/paper/2102.05011/) (2021)

## Built on by (most cited first, 1 of 1)

- [MANTLE: A Framework for Adaptive In-Situ Planetary Perception Using a Modular Uplink Principle](https://mlsanity.com/paper/2608.28724/) (2026)

## Abstract (arXiv)

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 CTX data. This approach discovered 69 new fresh impacts that have been confirmed with follow-up HiRISE images. We found that examining candidates partitioned by thermal inertia (TI) values, which is only possible due to the large number of machine learning candidates, helps reduce the observational bias and increase the number of known high-TI impacts.