First Dataset of Wind Turbine Data Created at National Level With Deep Learning Techniques From Aerial Orthophotographs With a Spatial Resolution of 0.5 M/Pixel

First Dataset of Wind Turbine Data Created at National Level With Deep Learning Techniques From Aerial Orthophotographs With a Spatial Resolution of 0.5 M/Pixel

Deep learning applied to feature extraction and mapping from high-resolution images is demonstrating the potential of this branch of data-intensive Artificial Intelligence to improve terrain mapping processes. The documented experiences have been applied on a small scale and there is a great expecta...

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Bibliographic Details
Main Authors: Miguel-Angel Manso-Callejo, Calimanut-Ionut Cira, Ramon Pablo Alcarria Garrido, Francisco Javier Gonzalez Matesanz
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Feature extraction
feature recognition
image classification
semantic segmentation
wind turbines
Online Access:https://ieeexplore.ieee.org/document/9507327/

Internet

https://ieeexplore.ieee.org/document/9507327/

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