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
Parents
38
SPOC: Deep Learning-based Terrain Classification for Mars Rover Missions
Brandon Rothrock, Ryan Kennedy, Chris Cunningham et al. · AIAA SPACE 2016 · 2016 · 159 citations
Deep Texture Manifold for Ground Terrain Recognition
Jia Xue, Hang Zhang, Kristin Dana · 2018 · 146 citations
Geometric and visual terrain classification for autonomous mobile navigation
Fabian Schilling, Xi Chen, John Folkesson et al. · 2017 · 77 citations
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Jet Propulsion Laboratory
Pasadena, United States
6 h-index
23 publications
1.4K citations
California Institute of Technology
Pasadena, United States
4 h-index
18 publications
702 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
9 publications
459 citations

Shenzhen University
Shenzhen, China
2 h-index
5 publications
117 citations
Rutgers Sexual and Reproductive Health and Rights
Utrecht, Netherlands
2 h-index
6 publications
468 citations

Centre National de la Recherche Scientifique
Paris, France
2 h-index
5 publications
221 citations
China University of Geosciences
Wuhan, China
2 h-index
8 publications
290 citations
Université Claude Bernard Lyon 1
Villeurbanne, France
2 h-index
9 publications
951 citations
1–8 of 68
3 publications · 96 citations
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TOG · 2 publications · 173 citations
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IJRR · 2 publications · 103 citations
2
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JFR · 1 publications · 65 citations
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1 publications · 58 citations
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