Spatial Decision Tree Learning

Spatial Decision Tree Learning is a machine learning method that extends conventional decision tree induction by incorporating spatial autocorrelation and spatial heterogeneity into the splitting criteria to improve predictive accuracy in geographically referenced data.

34

Publications

2.9K

Citations

86

Authors

54

Institutions

Publications per year

2017–2026

14

Authors

86

  1. IS
    Imas Sukaesih Sitanggang

    IPB University, Indonesia

    7 publications · 135 citations

    7

    h-index

  2. AN
    Andi Nurkholis

    Universitas Teknokrat Indonesia, Indonesia

    4 publications · 103 citations

    4

    h-index

  3. JK
    Joseph Knight

    University of Minnesota, United States

    3 publications · 108 citations

    3

    h-index

  4. ZJ
    Zhe Jiang

    University of Alabama, United States

    3 publications · 108 citations

    3

    h-index

  5. JC
    Jennifer Corcoran

    University of Minnesota, United States

    3 publications · 108 citations

    3

    h-index

1–5 of 86

Venues

  1. Environmental Management

    2 publications · 333 citations

    2

    h-index

  2. IEEE Transactions on Antennas and Propagation

    1 publications · 26 citations

    1

    h-index

  3. SOIL

    1 publications · 74 citations

    1

    h-index

  4. Journal of Hydrology

    1 publications · 840 citations

    1

    h-index

  5. Geomatics Natural Hazards and Risk

    1 publications · 11 citations

    1

    h-index

1–5