Kernel-based Spatial Features

Kernel-Based Spatial Features is a spatial feature engineering method that uses kernel functions to transform and capture localized spatial dependencies by mapping geographic points into a higher-dimensional space through weighted neighborhood interactions.

35

Publications

2.4K

Citations

112

Authors

56

Institutions

Publications per year

2017–2026

16

Authors

112

  1. ZW
    Zebin Wu

    Nanjing University of Science and Technology, China

    3 publications · 170 citations

    3

    h-index

  2. BJ
    Byeungwoo Jeon

    Sungkyunkwan University, South Korea

    2 publications · 160 citations

    2

    h-index

  3. LX
    Liang Xiao

    Nanjing University of Science and Technology, China

    2 publications · 21 citations

    2

    h-index

  4. DY
    Dong Yu

    Microsoft (United States), United States

    2 publications · 150 citations

    2

    h-index

  5. JA
    Jón Atli Benediktsson

    University of Iceland, Iceland

    2 publications · 289 citations

    2

    h-index

1–5 of 112

Venues

  1. 3

    h-index

  2. IEEE Transactions on Multimedia

    3 publications · 113 citations

    2

    h-index

  3. International Journal of Remote Sensing

    2 publications · 32 citations

    2

    h-index

  4. 2009 IEEE Conference on Computer Vision and Pattern Recognition

    Conference · 1 publications · 46 citations

    1

    h-index

  5. IEEE Transactions on Cybernetics

    1 publications · 69 citations

    1

    h-index

1–5