Now showing items 1-3 of 3
Unsupervised Domain Adaptation using Satellite Images for Significantly Different Infrastructure Objects
(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 ...
Improving LULC Map Production via Semantic Segmentation and Unsupervised Domain Adaptation
(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 ...
Automated Land Use and Land Cover Map Production: A Deep Learning Framework
(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 ...