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Oxia Planum, Mars, classified using the NOAH-H deep-learning terrain classification system

Author

Listed:
  • Alexander M. Barrett
  • Jack Wright
  • Elena Favaro
  • Peter Fawdon
  • Matthew R. Balme
  • Mark J. Woods
  • Spyros Karachalios
  • Eleni Bohacek
  • Elliot Sefton-Nash
  • Luc Joudrier

Abstract

We present a map of Oxia Planum, Mars, the landing site for the ExoMars Rover. This shows surface texture and aeolian bedform distribution, classified using a deep learning (DL) system. A hierarchical classification scheme was developed, categorising the surface textures observed at the site. This was then used to train a DL network, the ‘Novelty or Anomaly Hunter – HiRISE’ (NOAH-H). The DL applied the classification scheme across a wider area than could have been mapped manually. The result showed strong agreement with human-mapped areas reserved for validation. The resulting product is presented in two ways, representing the two principle levels of the classification scheme. ‘Descriptive classes’ are purely textural in nature, making them compatible with a machine learning approach. These are then combined into ‘interpretive groups’, broader thematic classes, which provide an interpretation of the landscape. This step allows for a more intuitive analysis of the results by human users.

Suggested Citation

  • Alexander M. Barrett & Jack Wright & Elena Favaro & Peter Fawdon & Matthew R. Balme & Mark J. Woods & Spyros Karachalios & Eleni Bohacek & Elliot Sefton-Nash & Luc Joudrier, 2023. "Oxia Planum, Mars, classified using the NOAH-H deep-learning terrain classification system," Journal of Maps, Taylor & Francis Journals, vol. 19(1), pages 2112777-211, December.
    • Handle: RePEc:taf:tjomxx:v:19:y:2023:i:1:p:2112777
    DOI: 10.1080/17445647.2022.2112777
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