Planetary Mapping Using Deep Learning: A Method to Evaluate Feature Identification Confidence Applied to Habitats in Mars-Analog Terrain.

The goals of Mars exploration are evolving beyond describing environmental habitability at global and regional scales to targeting specific locations for biosignature detection, sample return, and eventual human exploration. An increase in the specificity of scientific goals-from follow the water to find the biosignatures-requires parallel developments in strategies that translate terrestrial Mars-analog research into confident identification of rover-explorable targets on Mars. Precisely how to integrate terrestrial, ground-based analyses with orbital data sets and transfer those lessons into rover-relevant search strategies for biosignatures on Mars remains an open challenge. Here, leveraging small Unmanned Aerial System (sUAS) technology and state-of-the-art fully convolutional neural networks for pixel-wise classification, we present an end-to-end methodology that applies Deep Learning to map geomorphologic units and quantify feature identification confidence. We used this method to assess the identification confidence of rover-explorable habitats in the Mars-analog Salar de Pajonales over a range of spatial resolutions and found that spatial resolutions two times better than are available from Mars would be necessary to identify habitats in this study at the 1-σ (85%) confidence level. The approach we present could be used to compare the identifiability of habitats across Mars-analog environments and focus Mars exploration from the scale of regional habitability to the scale of specific habitats. Our methods could also be adapted to map dome- and ridge-like features on the surface of Mars to further understand their origin and astrobiological potential.