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Trepekli, Katerina
10  Ergebnisse:
Personensuche X
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1

Deep point cloud regression for above-ground forest biomass..:

Oehmcke, Stefan ; Li, Lei ; Trepekli, Katerina...
Remote Sensing of Environment.  302 (2024)  - p. 113968 , 2024
Link: https://doi.org/10.1016/..
 
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2

UAV-borne, LiDAR-based elevation modelling: a method for im..:

Trepekli, Katerina ; Balstrøm, Thomas ; Friborg, Thomas...
Natural Hazards.  113 (2022)  1 - p. 423-451 , 2022
Link: https://doi.org/10.1007/..
 
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3

Deep learning based 3D point cloud regression for estimatin..:

, In: Proceedings of the 30th International Conference on Advances in Geographic Information Systems,
Oehmcke, Stefan ; Li, Lei ; Revenga, Jaime C.... - p. 1-4 , 2022
Link: https://dl.acm.org/doi/1..
 
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4

Above-Ground Biomass Prediction for Croplands at a Sub-Mete..:

Revenga, Jaime C. ; Trepekli, Katerina ; Oehmcke, Stefan...
Remote Sensing.  14 (2022)  16 - p. 3912 , 2022
Link: https://doi.org/10.3390/..
 
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5

Deriving Aerodynamic Roughness Length at Ultra-High Resolut..:

Trepekli, Katerina ; Friborg, Thomas
Remote Sensing.  13 (2021)  17 - p. 3538 , 2021
Link: https://doi.org/10.3390/..
 
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6

Deep Learning Based 3D Point Cloud Regression for Estimatin..:

Oehmcke, Stefan ; Li, Lei ; Trepekli, Katerina...
http://arxiv.org/abs/2112.11335.  , 2021
Link: http://arxiv.org/abs/211..
 
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7

Above-Ground Biomass Prediction for Croplands at a Sub-Mete..:

Jaime C. Revenga ; Katerina Trepekli ; Stefan Oehmcke...
https://www.mdpi.com/2072-4292/14/16/3912.  , 2022
Link: https://doi.org/10.3390/..
 
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8

Above-Ground Biomass Prediction for Croplands at a Sub-Mete..:

Jaime C. Revenga ; Katerina Trepekli ; Stefan Oehmcke...
AI Remote Sensing.  , 2022
Link: https://doi.org/10.3390/..
 
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9

Deriving Aerodynamic Roughness Length at Ultra-High Resolut..:

Katerina Trepekli ; Thomas Friborg
https://www.mdpi.com/2072-4292/13/17/3538.  , 2021
Link: https://doi.org/10.3390/..
 
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10

Deriving Aerodynamic Roughness Length at Ultra-High Resolut..:

Katerina Trepekli ; Thomas Friborg
Biogeosciences Remote Sensing.  , 2021
Link: https://doi.org/10.3390/..
 
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