Transfer Learning For Geospatial Data

Transfer Learning for Geospatial Data is a machine learning methodology that adapts knowledge acquired from a source domain with abundant geospatial observations to improve model performance in a target domain with limited or distinct geospatial data, while accounting for spatial heterogeneity, scale differences, and geographic dependencies.

47

Publications

2.4K

Citations

170

Authors

91

Institutions

Publications per year

2017–2026

44

Authors

170

  1. DI
    Dino Ienco

    Territoires, Environnement, Télédétection et Information Spatiale

    3 publications · 75 citations

    3

    h-index

  2. NB
    Nicolas Baghdadi

    Territoires, Environnement, Télédétection et Information Spatiale

    3 publications · 75 citations

    3

    h-index

  3. RG
    Raffaele Gaetano

    Centre de Coopération Internationale en Recherche Agronomique pour le Développement, France

    2 publications · 45 citations

    2

    h-index

  4. MK
    Monika Kuffer

    University of Twente, Netherlands

    2 publications · 79 citations

    2

    h-index

  5. KM
    Kai Ma

    Sichuan University, China

    2 publications · 248 citations

    2

    h-index

1–5 of 170

Venues

  1. Remote Sensing

    5 publications · 174 citations

    5

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 46 citations

    3

    h-index

  3. Remote Sensing of Environment

    2 publications · 453 citations

    2

    h-index

  4. 2

    h-index

  5. Environmental Science & Technology

    1 publications · 24 citations

    1

    h-index

1–5