Multimodal Geospatial Fusion

Multimodal Geospatial Fusion is a geospatial data integration framework that combines heterogeneous data sources—such as remote sensing imagery, social media, sensor networks, and geographic information systems—distinguished by their disparate modalities, to generate enhanced spatial representations, analyses, or predictions through synergistic fusion techniques.

53

Publications

2.2K

Citations

282

Authors

129

Institutions

Publications per year

2017–2026

34

Authors

282

  1. NY
    Naoto Yokoya

    The University of Tokyo, Japan

    5 publications · 309 citations

    5

    h-index

  2. PG
    Pedram Ghamisi

    Helmholtz-Zentrum Dresden-Rossendorf, Germany

    4 publications · 218 citations

    4

    h-index

  3. RH
    Ronny Hänsch

    Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Germany

    3 publications · 73 citations

    3

    h-index

  4. JC
    Jocelyn Chanussot

    Institut polytechnique de Grenoble, France

    3 publications · 124 citations

    3

    h-index

  5. JL
    Jun Li

    Sun Yat-sen University, China

    3 publications · 124 citations

    3

    h-index

1–5 of 282

Venues

  1. Remote Sensing

    5 publications · 145 citations

    5

    h-index

  2. 4

    h-index

  3. Remote Sensing of Environment

    3 publications · 246 citations

    3

    h-index

  4. 3

    h-index

  5. European Journal of Remote Sensing

    2 publications · 39 citations

    2

    h-index

1–5