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Using Machine Learning to Reduce Observational Biases When Detecting New Impacts on Mars
This study investigates the use of a trained machine learning classifier to increase the detection of fresh impacts on Mars using CTX data.
Unsupervised Distribution Learning for Lunar Surface Anomaly Detection
In this work we show that modern data-driven machine learning techniques can be successfully applied on lunar surface remote sensing data to learn, in an unsupervised way, sufficiently good representations of the data distribution to enable lunar technosignature and anomaly detection.
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