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http://dx.doi.org/10.12652/Ksce.2020.40.5.0535

Automatic Generation of Land Cover Map Using Residual U-Net  

Yoo, Su Hong (Yonsei University)
Lee, Ji Sang (Yonsei University)
Bae, Jun Su (Yonsei University)
Sohn, Hong Gyoo (Yonsei University)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.40, no.5, 2020 , pp. 535-546 More about this Journal
Abstract
Land cover maps are derived from satellite and aerial images by the Ministry of Environment for the entire Korea since 1998. Even with their wide application in many sectors, their usage in research community is limited. The main reason for this is the map compilation cycle varies too much over the different regions. The situation requires us a new and quicker methodology for generating land cover maps. This study was conducted to automatically generate land cover map using aerial ortho-images and Landsat 8 satellite images. The input aerial and Landsat 8 image data were trained by Residual U-Net, one of the deep learning-based segmentation techniques. Study was carried out by dividing three groups. First and second group include part of level-II (medium) categories and third uses group level-III (large) classification category defined in land cover map. In the first group, the results using all 7 classes showed 86.6 % of classification accuracy The other two groups, which include level-II class, showed 71 % of classification accuracy. Based on the results of the study, the deep learning-based research for generating automatic level-III classification was presented.
Keywords
Aerial ortho photo; Landsat 8; Residual U-Net; Automatic land cover map generation;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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