Feature Selection For Spatial Data

Feature Selection for Spatial Data is a methodological process in spatial feature engineering that identifies and retains the most relevant spatial and non-spatial attributes from a dataset while accounting for spatial autocorrelation, heterogeneity, and scale, thereby distinguishing it from conventional feature selection techniques that ignore spatial dependencies.

27

Publications

2.5K

Citations

89

Authors

58

Institutions

Publications per year

2017–2026

15

Authors

89

  1. CR
    Christoph Reudenbach

    Philipps University of Marburg, Germany

    2 publications · 953 citations

    2

    h-index

  2. HM
    Hanna Meyer

    Philipps University of Marburg, Germany

    2 publications · 953 citations

    2

    h-index

  3. JJ
    Jian‐Hua Jia

    Sun Yat-sen University, China

    1 publications · 55 citations

    1

    h-index

  4. MT
    Maggie Tran

    Geoscience Australia, Australia

    1 publications · 96 citations

    1

    h-index

  5. IS
    Izuru Saizen

    Kyoto University, Japan

    1 publications · 35 citations

    1

    h-index

1–5 of 89

Venues

  1. Advances in Data Analysis and Classification

    2 publications · 28 citations

    2

    h-index

  2. Remote Sensing of Environment

    1 publications · 143 citations

    1

    h-index

  3. Frontiers in Plant Science

    1 publications · 11 citations

    1

    h-index

  4. 1

    h-index

  5. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 1 publications · 18 citations

    1

    h-index

1–5