Zobrazeno 1 - 5
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pro vyhledávání: '"Galen Richardson"'
Autor:
Julie Lovitt, Galen Richardson, Krishan Rajaratnam, Wenjun Chen, Sylvain G. Leblanc, Liming He, Scott E. Nielsen, Ashley Hillman, Isabelle Schmelzer, André Arsenault
Publikováno v:
Canadian Journal of Remote Sensing, Vol 48, Iss 6, Pp 849-872 (2022)
High-quality ground-truth data are critical for developing reliable Earth Observation (EO) based geospatial products. Conventional methods of collecting these data are either subject to an unknown amount of human error and bias or require extended ti
Externí odkaz:
https://doaj.org/article/cb39beba15a04e7b8ebf23afaa3098a4
Publikováno v:
Remote Sensing, Vol 15, Iss 22, p 5307 (2023)
Estimating the number of trees within a forest stand, i.e., the forest stand density (FSD), is challenging at large scales. Recently, researchers have turned to a combination of remote sensing and machine learning techniques to derive these estimates
Externí odkaz:
https://doaj.org/article/15d62d25f1b94d5fb56917f642512267
Autor:
Galen Richardson, Anders Knudby, Wenjun Chen, Michael Sawada, Julie Lovitt, Liming He, Leila Yousefizadeh Naeni
Publikováno v:
PLoS ONE, Vol 18, Iss 11, p e0292839 (2023)
Lichen mapping is vital for caribou management plans and sustainable land conservation. Previous studies have used random forest, dense neural network, and convolutional neural network models for mapping lichen coverage. However, to date, it is not c
Externí odkaz:
https://doaj.org/article/1f0e1cad98564525a6f8fd2c5cd0ccb6
Publikováno v:
Drones, Vol 5, Iss 3, p 99 (2021)
Relating ground photographs to UAV orthomosaics is a key linkage required for accurate multi-scaled lichen mapping. Conventional methods of multi-scaled lichen mapping, such as random forest models and convolutional neural networks, heavily rely on p
Externí odkaz:
https://doaj.org/article/0060d4da8894455ab03ca28e4c510740
Publikováno v:
Drones
Volume 5
Issue 3
Drones, Vol 5, Iss 99, p 99 (2021)
Volume 5
Issue 3
Drones, Vol 5, Iss 99, p 99 (2021)
Relating ground photographs to UAV orthomosaics is a key linkage required for accurate multi-scaled lichen mapping. Conventional methods of multi-scaled lichen mapping, such as random forest models and convolutional neural networks, heavily rely on p