Spatial Machine Learning

Spatial Machine Learning is a computational methodology that extends conventional machine learning approaches to explicitly model and analyze spatial dependencies, heterogeneities, and patterns inherent in geographically referenced data.

333

Publications

18.4K

Citations

1.2K

Authors

540

Institutions

Publications per year

2017–2026

261

Authors

1.2K

  1. ZJ
    Zhe Jiang

    University of Alabama, United States

    9 publications · 335 citations

    9

    h-index

  2. HM
    Hanna Meyer

    Philipps University of Marburg, Germany

    6 publications · 736 citations

    6

    h-index

  3. MK
    Mikhaïl Kanevski

    University of Lausanne, Switzerland

    4 publications · 137 citations

    4

    h-index

  4. MH
    Marco Helbich

    Utrecht University, Netherlands

    4 publications · 274 citations

    4

    h-index

  5. MK
    Monika Kuffer

    University of Twente, Netherlands

    4 publications · 123 citations

    4

    h-index

1–5 of 1.2K

Venues

  1. 13

    h-index

  2. ISPRS International Journal of Geo-Information

    IJGI · 10 publications · 378 citations

    10

    h-index

  3. Sustainable Cities and Society

    8 publications · 400 citations

    8

    h-index

  4. Remote Sensing

    8 publications · 209 citations

    8

    h-index

  5. The Science of The Total Environment

    7 publications · 744 citations

    7

    h-index

1–5