Geospatial Data Cleaning

Geospatial Data Cleaning is a systematic process in mine survey data processing that identifies, corrects, and eliminates errors, inconsistencies, and inaccuracies in spatial datasets to ensure geometric and attribute-level integrity, thereby enhancing the reliability and usability of geospatial information for downstream analysis and decision-making.

10

Publications

223

Citations

37

Authors

18

Institutions

Publications per year

2017–2026

5

Authors

37

  1. GR
    G. Randy Keller

    University of Oklahoma, United States

    2 publications · 29 citations

    2

    h-index

  2. RL
    Rafael Loyola

    Universidade Federal de Goiás, Brazil

    1 publications · 75 citations

    1

    h-index

  3. XQ
    Xinlin Qian

    State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, China

    1 publications · 14 citations

    1

    h-index

  4. LJ
    Lucas Jardim

    Universidade Federal de Goiás, Brazil

    1 publications · 75 citations

    1

    h-index

  5. WT
    Wataru Takeuchi

    The University of Tokyo, Japan

    1 publications · 14 citations

    1

    h-index

1–5 of 37

Venues

  1. Computers & Geosciences

    CAGEO · 1 publications · 10 citations

    1

    h-index

  2. Proceedings

    IV · 1 publications · 39 citations

    1

    h-index

  3. Sensors

    1 publications · 17 citations

    1

    h-index

  4. ISPRS International Journal of Geo-Information

    IJGI · 1 publications · 12 citations

    1

    h-index

  5. Methods in Ecology and Evolution

    1 publications · 75 citations

    1

    h-index

1–5