Jezero crater, Mars: application of the deep learning NOAH-H terrain classification system

Wright, Jack; Barrett, Alexander M.; Fawdon, Peter; Favaro, Elena A.; Balme, Matthew R.; Woods, Mark J. and Karachalios, Spyros (2022). Jezero crater, Mars: application of the deep learning NOAH-H terrain classification system. Journal of Maps, 18(2) pp. 484–496.

DOI: https://doi.org/10.1080/17445647.2022.2095935

Abstract

We applied a deep learning terrain classification system, the ‘Novelty or Anomaly Hunter – HiRISE’ (NOAH-H), originally developed for the ExoMars landing sites in Oxia Planum and Mawrth Vallis, to the Mars 2020 Perseverance rover landing site in Jezero crater. NOAH-H successfully classified the terrain in four HiRISE images of Jezero even though the landforms in the Jezero study area were slightly different from those in the training dataset. We mosaicked the NOAH-H classified rasters and compared them with a manually generated photogeological map, and with Perseverance rover and Ingenuity helicopter images. We find that grouped NOAH-H classes correspond well with the humanmade map and that individual classes are corroborated by the available ground-truth images. We conclude that our NOAH-H products can be refined for feeding into traversability analysis of the ExoMars Rosalind Franklin rover landing site at Oxia Planum and that they can also be used to aid the photogeological mapping process.

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About

• Item ORO ID
• 84535
• Item Type
• Journal Item
• ISSN
• 1744-5647
• Project Funding Details

• Funded Project Name	Project ID	Funding Body
  Planetary Science at the Open University 2020-2023	ST/T000228/1	STFC
  Novelty or Anomaly Hunter (NOAH)	4000118843/16/NL/ LvH1145 – Novelty or Anomaly Hunter (NOAH)	European Space Agency
  Not Set	ST/V001965/1	UKSA UK Space Agency
  UK ExoMars Rover Landing Site Science and Characterisation	ST/R001413/1	UKSA UK Space Agency
  Not Set	ST/T002913/1	UKSA UK Space Agency

• Keywords
• Mars surface; geomorphology; Jezero crater; machine learning; 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)
• Research Group
• ?? space ??
• Copyright Holders
• © 2022 The Authors
• Related URLs
• • https://doi.org/10.21954/ou.rd.17121482....(Research Dataset)
  • https://mars.nasa.gov/mars2020/multimedi...(Other)
  • https://www.uahirise.org/(Other)
  • https://astrogeology.usgs.gov/search/map...(Other)
  • https://astrogeology.usgs.gov/search/map...(Other)
• Depositing User
• Jack Wright