Terrain Representation Learning

Terrain Representation Learning is a computational framework that systematically captures and encodes the intrinsic geometric and topographic characteristics of terrain surfaces into structured latent representations to facilitate downstream geospatial analysis and interpretation tasks.

38

Publications

1.8K

Citations

148

Authors

68

Institutions

Publications per year

2017–2026

25

Authors

148

  1. LM
    Larry Matthies

    Jet Propulsion Laboratory, United States

    3 publications · 110 citations

    3

    h-index

  2. AC
    Aaron Courville

    Université de Montréal, Canada

    2 publications · 139 citations

    2

    h-index

  3. MH
    Marco Hutter

    ETH Zurich, Switzerland

    2 publications · 63 citations

    2

    h-index

  4. JX
    Jia Xue

    Rutgers Sexual and Reproductive Health and Rights, Netherlands

    2 publications · 156 citations

    2

    h-index

  5. AP
    Adrien Peytavie

    Université Claude Bernard Lyon 1, France

    2 publications · 200 citations

    2

    h-index

1–5 of 148

Venues

  1. Remote Sensing

    3 publications · 96 citations

    3

    h-index

  2. ACM Transactions on Graphics

    TOG · 2 publications · 173 citations

    2

    h-index

  3. The International Journal of Robotics Research

    IJRR · 2 publications · 103 citations

    2

    h-index

  4. Journal of Field Robotics

    JFR · 1 publications · 65 citations

    1

    h-index

  5. Acta Astronautica

    1 publications · 58 citations

    1

    h-index

1–5