Now showing items 1-3 of 3

    • Automated LULC Map Production using Deep Neural Networks 

      Henry, Christopher J.; Storie, Christopher; Palaniappan, Muthu; Alhassan, Victor; Swamy, Mallikarjun; Aleshinloye, Damilola; Curtis, Andrew; Kima, Daeyoun (Taylor & Francis, 2019-01-17)
      This article presents an approach to automating the creation of land-use/land-cover classification (LULC) maps from satellite images using deep neural networks that were developed to perform semantic segmentation of natural ...
    • Improving LULC Map Production via Semantic Segmentation and Unsupervised Domain Adaptation 

      Tsenov, Rostyslav-Mykola (University of WinnipegUniversity 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 ...
    • Unsupervised Domain Adaptation using Satellite Images for Significantly Different Infrastructure Objects 

      Sokolov, Mikhail (University of WinnipegUniversity 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 ...