Guiding Field Exploration on Earth and Mars with Outlier Detection

Analyzing remote sensing datasets to identify sites of interest for in-situ investigation in robotic and human exploration is challenging with current methods. In this study, we investigated the utility of machine learning algorithms for outlier detection to identify samples of interest at field exploration sites based on remotely-sensed observations. In our analysis of two study sites, Cucomungo alluvial fan in Death Valley, CA and Jezero crater on Mars, we found that outliers identified by the isolation forest algorithm in satellite datasets seemed to correspond to unique compositions or surface properties. Our results show that outlier detection is a promising method for assisting scientists by guiding field exploration site selection.