Geospatial Representation Learning
Geospatial Representation Learning is a machine learning paradigm that, as a subcategory of representation learning, focuses on automatically discovering task-invariant, structured embeddings of geospatial entities by leveraging their geometric, topological, and semantic properties inherent in geographic data.
126
Publications
6.3K
Citations
441
Authors
172
Institutions
Publications per year
2017–2026
104
126
1–5 of 126
441
8
h-index
University at Buffalo, State University of New York, United States
7 publications · 924 citations
7
h-index
6
h-index
5
h-index
University of California, Santa Barbara, United States
5 publications · 788 citations
5
h-index
1–5 of 441
172
University at Buffalo, State University of New York
Buffalo, United States
6 h-index
11 publications
1.5K citations
Stanford University
Stanford, United States
6 h-index
22 publications
2K citations
University of California, Santa Barbara
Santa Barbara, United States
6 h-index
15 publications
1.5K citations
Nanyang Technological University
Singapore, Singapore
5 h-index
13 publications
575 citations
University of Wisconsin–Madison
Madison, United States
5 h-index
5 publications
847 citations
University of Lisbon
Lisbon, Portugal
4 h-index
6 publications
126 citations
Wuhan University
Wuhan, China
4 h-index
9 publications
476 citations

University of Georgia
Athens, United States
4 h-index
9 publications
841 citations
1–8 of 172
Repository · 11 publications · 335 citations
11
h-index
8 publications · 930 citations
8
h-index
Conference · 5 publications · 297 citations
5
h-index
5 publications · 147 citations
5
h-index
5 publications · 145 citations
5
h-index
1–5