Remote Sensing Feature Derivation
Remote Sensing Feature Derivation is a methodological framework within geospatial feature engineering that systematically extracts and transforms spectral, spatial, and temporal information from remote sensing data into meaningful, higher-level features through computational algorithms, distinguishing it from general feature extraction by its specific focus on sensor-derived Earth observation data.
9
Publications
375
Citations
36
Authors
18
Institutions
Publications per year
2017–2026
3
9
1–5 of 9
36
1
h-index
1
h-index
1
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h-index
1–5 of 36
18
GeoInformation (United Kingdom)
Cambridge, United Kingdom
1 h-index
1 publications
115 citations
Mongolian Academy of Sciences
Ulaanbaatar, Mongolia
1 h-index
4 publications
460 citations
University of California, Davis
Davis, United States
1 h-index
2 publications
70 citations
Institute of Space and Astronautical Science
Tokyo, Japan
1 h-index
1 publications
20 citations

Hanscom Air Force Base
Bedford, United States
1 h-index
1 publications
47 citations

Ocean University of China
Qingdao, China
1 h-index
2 publications
48 citations

University of Nottingham
Nottingham, United Kingdom
1 h-index
1 publications
24 citations
GFZ Helmholtz Centre for Geosciences
Potsdam, Germany
1 h-index
5 publications
275 citations
1–8 of 18
1 publications · 45 citations
1
h-index
1 publications · 20 citations
1
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GRSL · 1 publications · 24 citations
1
h-index
1 publications · 47 citations
1
h-index
1 publications · 35 citations
1
h-index
1–5