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

Barrett, Alexander M.; Wright, Jack; Favaro, Elena; Fawdon, Peter; Balme, Matthew R.; Woods, Mark J.; Karachalios, Spyros; Bohachek, Eleni; Sefton-Nash, Elliot and Joudrier, Luc (2022). Oxia Planum, Mars, classified using the NOAH-H deep-learning terrain classification system. Journal of Maps (Early Access).

DOI: https://doi.org/10.1080/17445647.2022.2112777

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.

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About

• Item ORO ID
• 84631
• Item Type
• Journal Item
• ISSN
• 1744-5647
• Project Funding Details

• Funded Project Name	Project ID	Funding Body
  Not Set	Not Set	UK Space Agency (UKSA)
  Not Set	ST/T000228/1	STFC (Science & Technology Facilities Council)
  Novelty or Anomaly Hunter (NOAH)	4000118843/16/NL/LvH1145	ESA (European Space Agency)
  Not Set	ST/T002913/1	UK Space Agency (UKSA)
  Not Set	ST/V001965/1	UK Space Agency (UKSA)
  Not Set	ST/R001413/1	UK Space Agency (UKSA)
  Not Set	ST/V001965/1	UK Space Agency (UKSA)
  Not Set	ST/R001413/1	UK Space Agency (UKSA)

• Keywords
• Machine learning; Mars surface; geomorphology; ExoMars; deep learning; rover planning
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Physical Sciences
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2022 The Author(s)
• Depositing User
• Alexander Barrett