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

Authors

23

  1. HM
    Helmut Mayer

    Universität der Bundeswehr München, Germany

    2 publications · 52 citations

    2

    h-index

  2. AF
    Andrea Forberg

    Universität der Bundeswehr München, Germany

    2 publications · 111 citations

    2

    h-index

  3. KH
    Kaiyu Hang

    KTH Royal Institute of Technology, Sweden

    1 publications · 35 citations

    1

    h-index

  4. AA
    Alias Abdul Rahman

    University of Technology Malaysia, Malaysia

    1 publications · 16 citations

    1

    h-index

  5. GD
    Gregory Dudek

    McGill University, Canada

    1 publications · 11 citations

    1

    h-index

1–5 of 23

Venues

  1. 3

    h-index

  2. GeoJournal

    1 publications · 16 citations

    1

    h-index

  3. arXiv (Cornell University)

    Repository · 1 publications · 13 citations

    1

    h-index

  4. 1

    h-index

1–4