Multimodal Geospatial Data Fusion is the integrated analysis of heterogeneous geospatial data from multiple sensory or representational modalities, distinguished by its capacity to jointly exploit complementary spatial, spectral, temporal, and contextual information for enhanced environmental or urban understanding.
68
Publications
3.5K
Citations
258
Authors
139
Institutions
Publications per year
2017–2026
52
Parents
Publications
68
Gemine Vivone, Mauro Dalla Mura, Andrea Garzelli et al. · IEEE Geoscience and Remote Sensing Magazine · 2020 · 335 citations
1–5 of 68
Authors
258
- PG
3
h-index
- DT
3
h-index
- SD
3
h-index
- JL
3
h-index
- GM
2
h-index
1–5 of 258
Institutions
139
China University of Geosciences
Wuhan, China
6 h-index
12 publications
668 citations
Sun Yat-sen University
Guangzhou, China
5 h-index
11 publications
516 citations
Chinese Academy of Sciences
Beijing, China
5 h-index
13 publications
791 citations
Technical University of Munich
Munich, Germany
4 h-index
7 publications
269 citations
Peking University
Beijing, China
4 h-index
9 publications
856 citations
Tongji University
Shanghai, China
3 h-index
4 publications
72 citations
University of Genoa
Genoa, Italy
3 h-index
5 publications
256 citations
Wageningen University & Research
Wageningen, The Netherlands
3 h-index
4 publications
555 citations
1–8 of 139
Venues
- Remote Sensing
8 publications · 413 citations
8
h-index
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
4 publications · 294 citations
4
h-index
- ISPRS Journal of Photogrammetry and Remote Sensing
3 publications · 520 citations
4
h-index
- International Journal of Applied Earth Observation and Geoinformation
JAG · 3 publications · 119 citations
4
h-index
- IEEE Transactions on Geoscience and Remote Sensing
3 publications · 72 citations
3
h-index
1–5