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
Parents
53
MODIS–Landsat fusion for large area 30 m burned area mapping
Luigi Boschetti, David P. Roy, Christopher O. Justice et al. · Remote Sensing of Environment · 2015 · 170 citations
Naoto Yokoya, Pedram Ghamisi, Junshi Xia et al. · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2018 · 145 citations · Full text
1–5 of 53
282
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Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Germany
3 publications · 73 citations
3
h-index
3
h-index
3
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1–5 of 282
129

Chinese Academy of Sciences
Beijing, China
5 h-index
10 publications
440 citations

Beijing Normal University
Beijing, China
4 h-index
12 publications
382 citations
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
Cologne, Germany
4 h-index
5 publications
265 citations
RIKEN Center for Advanced Intelligence Project
Tokyo, Japan
4 h-index
4 publications
218 citations
The University of Tokyo
Tokyo, Japan
4 h-index
4 publications
296 citations
University of Chinese Academy of Sciences
Beijing, China
3 h-index
7 publications
280 citations
China University of Geosciences
Wuhan, China
3 h-index
11 publications
511 citations

Helmholtz-Zentrum Dresden-Rossendorf
Dresden, Germany
3 h-index
3 publications
73 citations
1–8 of 129
5 publications · 145 citations
5
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4 publications · 234 citations
4
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3 publications · 246 citations
3
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3 publications · 156 citations
3
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2 publications · 39 citations
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