3D Data Generalization
3D Data Generalization is a geographic information science process that systematically reduces the level of detail or complexity in three-dimensional spatial data while preserving essential geometric, topological, and semantic characteristics for representation at varying scales or resolutions.
11
Publications
316
Citations
23
Authors
9
Institutions
Publications per year
2017–2026
1
11
1–5 of 11
23
2
h-index
2
h-index
1
h-index
1
h-index
1
h-index
1–5 of 23
9
Universität der Bundeswehr München
Munich, Germany
3 h-index
3 publications
152 citations
University of Technology Malaysia
Johor Bahru, Malaysia
1 h-index
2 publications
32 citations

KTH Royal Institute of Technology
Stockholm, Sweden
1 h-index
5 publications
175 citations

Delft University of Technology
Delft, The Netherlands
1 h-index
2 publications
34 citations
Leibniz University of Applied Sciences
Hanover, Germany
1 h-index
2 publications
22 citations

McGill University
Montreal, Canada
1 h-index
3 publications
33 citations
University of Potsdam
Potsdam, Germany
1 h-index
2 publications
28 citations
Wuhan University
Wuhan, China
1 h-index
1 publications
47 citations
1–8 of 9
3 publications · 105 citations
3
h-index
1 publications · 16 citations
1
h-index
Repository · 1 publications · 13 citations
1
h-index
1 publications · 100 citations
1
h-index
1–4