Deep Learning-based Data Fusion

Deep Learning-Based Data Fusion is a computational approach within geospatial data fusion that leverages deep neural networks to automatically integrate heterogeneous spatial data sources by learning hierarchical feature representations and their contextual relationships.

527

Publications

32.7K

Citations

2.3K

Authors

800

Institutions

Publications per year

2017–2026

511

Authors

2.3K

  1. XX
    Xiao Xiang Zhu

    Technical University of Munich, Germany

    8 publications · 1.4K citations

    8

    h-index

  2. JC
    Jocelyn Chanussot

    Institut polytechnique de Grenoble, France

    7 publications · 1.1K citations

    7

    h-index

  3. JL
    Jun Li

    Sun Yat-sen University, China

    6 publications · 390 citations

    6

    h-index

  4. BL
    Bertrand Le Saux

    European Space Research Institute, Italy

    6 publications · 320 citations

    6

    h-index

  5. LW
    Lizhe Wang

    China University of Geosciences, China

    5 publications · 294 citations

    5

    h-index

1–5 of 2.3K

Venues

  1. 30

    h-index

  2. Remote Sensing

    45 publications · 2.2K citations

    26

    h-index

  3. 23

    h-index

  4. Information Fusion

    14 publications · 2.2K citations

    14

    h-index

  5. Sensors

    14 publications · 553 citations

    13

    h-index

1–5