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Edmundo Favaro

Biographic Data

ID790538
NAMEEdmundo Favaro
GIVEN NAMESEdmundo
FAMILY NAMEFavaro
SIGNATUREFAVARO E
AFFILIATIONSThe Open University
ORCID0000-0001-6126-5465
VERIFIEDYes
TOTAL WORKS5
TOTAL CITATIONS0
WORKS AS AUTHOR5
WORKS AS EDITOR0
FIRST PUBLICATION YEAR1951
LATEST PUBLICATION YEAR2024
H-INDEX0
  • The high-resolution map of Oxia Planum, Mars; the landing site of the ExoMars Rosalind Franklin rover mission

    Open Access•Peter Fawdon, Csilla Orgel et al.•ARTICLE•Journal of Maps•2024

    This 1:30,000 scale geological map describes Oxia Planum, Mars, the landing site for the ExoMars Rosalind Franklin rover mission. The map represents our current understanding of bedrock units and their relationships prior to Rosalind Franklin’s exploration of this location. The map details 15 bedrock units organised into 6 groups and 7 textural and surficial units. The bedrock units were identified using visible and near-infrared remote sensing d…

  • 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…

  • Jezero crater, Mars

    Open Access•Jack Wright, Alexander M Barrett et al.•ARTICLE•Journal of Maps•2022

    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 …

  • Dámaso Antonio Larrañaga, su vida y su época

    Madaline W Nichols, Edmundo Favaro•ARTICLE•Hispanic American Historical Review•1951

No prominent works on this page.

  • Dámaso Antonio Larrañaga, su vida y su época

    Madaline W Nichols, Edmundo Favaro•ARTICLE•Hispanic American Historical Review•1951

  • Jezero crater, Mars

    Open Access•Jack Wright, Alexander M Barrett et al.•ARTICLE•Journal of Maps•2022

    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 …

  • 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…

  • The high-resolution map of Oxia Planum, Mars; the landing site of the ExoMars Rosalind Franklin rover mission

    Open Access•Peter Fawdon, Csilla Orgel et al.•ARTICLE•Journal of Maps•2024

    This 1:30,000 scale geological map describes Oxia Planum, Mars, the landing site for the ExoMars Rosalind Franklin rover mission. The map represents our current understanding of bedrock units and their relationships prior to Rosalind Franklin’s exploration of this location. The map details 15 bedrock units organised into 6 groups and 7 textural and surficial units. The bedrock units were identified using visible and near-infrared remote sensing d…

Astro and Planetary Science (4 works) · Geology (4 works) · Mars Exploration Program (4 works) · Planetary Science and Exploration (4 works) · Artificial Intelligence (3 works) · Astrobiology (3 works) · Cartography (3 works) · Computer Science (3 works) · Geography (3 works) · Geomorphology (3 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