Jezero crater, Mars: application of the deep learning NOAH-H terrain classification system. Issue 2 (1st December 2022)

• Record Type: Journal Article
• Authors: Wright, Jack; Barrett, Alexander M.; Fawdon, Peter; Favaro, Elena A.; Balme, Matthew R.; Woods, Mark J.; Karachalios, Spyros
• Is Part Of: Journal of maps. Volume 18:Issue 2(2022)
• Page Start: 484
• Page End: 496
• Publication Date: 2022-12-01
• Subjects: Mars' surface -- geomorphology -- Jezero crater -- machine learning -- deep learning -- rover planning; Maps -- Periodicals; Cartography -- Periodicals; 912.05
• DOI: 10.1080/17445647.2022.2095935
• Languages: English
• ISSNs: 1744-5647

Abstract

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.