Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Luc Joudrier

Biographic Data

ID8536342
NAMELuc Joudrier
GIVEN NAMESLuc
FAMILY NAMEJoudrier
SIGNATUREJOUDRIER L
AFFILIATIONSEuropean Space Research and Technology Centre
VERIFIEDNo
TOTAL WORKS2
TOTAL CITATIONS0
WORKS AS AUTHOR2
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2023
LATEST PUBLICATION YEAR2023
H-INDEX0
  • Oxia Planum, Mars, classified using the Noah-H deep-learning terrain classification system

    Open Access•Alexander M Barrett, Jack Wright et al.•ARTICLE•Journal of Maps•2023

    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 th…

  • Mawrth Vallis, Mars, classified using the Noah-H deep-learning terrain classification system

    Open Access•Alexander M Barrett, Peter Fawdon et al.•ARTICLE•Journal of Maps•2023

    A deep learning (DL) terrain classification system, the Novelty and Anomaly Hunter – HiRISE (NOAH-H) was used to produce a terrain map of Mawrth Vallis, Mars. With it, we digitised the extent and distribution of transverse aeolian ridges (TARs), a common type of martian aeolian bedform. We present maps of the site, classifying terrain into descriptive classes and interpretive groups. TAR density maps are calculated, and the network output is comp…

No prominent works on this page.

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

    Open Access•Alexander M Barrett, Jack Wright et al.•ARTICLE•Journal of Maps•2023

    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 th…

  • Mawrth Vallis, Mars, classified using the Noah-H deep-learning terrain classification system

    Open Access•Alexander M Barrett, Peter Fawdon et al.•ARTICLE•Journal of Maps•2023

    A deep learning (DL) terrain classification system, the Novelty and Anomaly Hunter – HiRISE (NOAH-H) was used to produce a terrain map of Mawrth Vallis, Mars. With it, we digitised the extent and distribution of transverse aeolian ridges (TARs), a common type of martian aeolian bedform. We present maps of the site, classifying terrain into descriptive classes and interpretive groups. TAR density maps are calculated, and the network output is comp…

Artificial Intelligence (2 works) · Astro and Planetary Science (2 works) · Cartography (2 works) · Computer Science (2 works) · Geography (2 works) · Geology (2 works) · Mars Exploration Program (2 works) · Planetary Science and Exploration (2 works) · Remote sensing (2 works) · Terrain (2 works)

Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae