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Unsupervised Domain Adaptation using Satellite Images for Significantly Different Infrastructure Objects 

Sokolov, Mikhail (University of Winnipeg, 2022-03-28)
Deep learning has become one of the most efficient computer vision tools in recent years. The success and variety of deep learning semantic segmentation models inspired scientists in the remote sensing domain to apply them ...
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Improving LULC Map Production via Semantic Segmentation and Unsupervised Domain Adaptation 

Tsenov, Rostyslav-Mykola (University of Winnipeg, 2021-04-13)
In recent years, a lot of remote sensing problems benefited from the improvements made in deep learning. In particular, deep learning semantic segmentation algorithms have provided improved frameworks for the automated ...
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Automated Land Use and Land Cover Map Production: A Deep Learning Framework 

Alhassan, Victor (University of Winnipeg, 2018-10-19)
In this thesis, we present an approach to automating the creation of land use and land cover (LULC) maps from satellite images using deep neural networks that were developed to perform semantic segmentation of natural ...

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AuthorAlhassan, Victor (1)Sokolov, Mikhail (1)Tsenov, Rostyslav-Mykola (1)Subject
Satellite images (3)
Convolutional neural network (CNN) (2)Deep learning (DL) (2)Land use and land cover (LULC) (2)Remote sensing (2)Semantic segmentation (2)Classification (1)Deep Convolutional Neural Networks (1)Deep Learning (1)Domain adaptation (1)... View MoreDate Issued2018 (1)2021 (1)2022 (1)Has File(s)Yes (3)

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