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Planetary Mapping Using Deep Learning: A Method to Evaluate Feature Identification Confidence Applied to Habitats in

Michael S Phillips1, Jeffrey E Moersch2, Nathalie A Cabrol3

  • 1Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, USA.

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Mapping Mars habitats requires higher resolution data. This study used deep learning on drone imagery to show that spatial resolutions twice as good as current Mars orbital data are needed for confident identification of potential life-supporting locations.

Keywords:
Confidence ScoreDeep LearningHabitat. Astrobiology 23, 76–93Mars analogThreshold of identification

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Area of Science:

  • Planetary Science
  • Astrobiology
  • Artificial Intelligence

Background:

  • Mars exploration goals are shifting towards specific biosignature detection and human exploration.
  • Translating terrestrial analog research to identify rover-explorable targets on Mars is a key challenge.
  • Integrating ground-based analysis with orbital data for Mars rover missions needs improved strategies.

Purpose of the Study:

  • To develop and present an end-to-end methodology for mapping geomorphologic units and quantifying feature identification confidence using Deep Learning.
  • To assess the identification confidence of rover-explorable habitats in a Mars-analog environment.
  • To determine the necessary spatial resolution for confident habitat identification on Mars.

Main Methods:

  • Utilized small Unmanned Aerial System (sUAS) technology for high-resolution imagery acquisition.
  • Applied state-of-the-art fully convolutional neural networks for pixel-wise classification.
  • Quantified feature identification confidence across various spatial resolutions in the Salar de Pajonales analog.

Main Results:

  • Developed a Deep Learning methodology to map geomorphologic units and assess identification confidence.
  • Found that spatial resolutions twice as good as current Mars orbital data are necessary for 85% confidence in habitat identification.
  • Demonstrated the approach's utility in comparing habitat identifiability across different Mars-analog environments.

Conclusions:

  • The presented methodology can refine Mars exploration strategies from regional habitability to specific habitat targeting.
  • The approach can be adapted to map Martian surface features like domes and ridges to study their origin and astrobiological potential.
  • Higher spatial resolution data is crucial for confident identification of potential habitats for future Mars missions.