Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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Application of a Deep Learning Method on Aerial Orthophotos to Extract Land Categories

  • Won, Taeyeon (Dept. of Advanced Technology Fusion, Konkuk University) ;
  • Song, Junyoung (Dept. of Civil and Environmental Engineering, Konkuk University) ;
  • Lee, Byoungkil (Dept. of Civil Engineering, Kyonggi University) ;
  • Pyeon, Mu Wook (Dept. of Civil and Environmental Engineering, Konkuk University) ;
  • Sa, Jiwon (Dept. of Digital Culture & Contents, Konkuk University)
  • Received : 2020.09.23
  • Accepted : 2020.10.26
  • Published : 2020.10.31
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Abstract

The automatic land category extraction method was proposed, and the accuracy was evaluated by learning the aerial photo characteristics by land category in the border area with various restrictions on the acquisition of geospatial data. As experimental data, this study used four years' worth of published aerial photos as well as serial cadastral maps from the same time period. In evaluating the results of land category extraction by learning features from different temporal and spatial ranges of aerial photos, it was found that land category extraction accuracy improved as the temporal and spatial ranges increased. Moreover, the greater the diversity and quantity of provided learning images, the less the results were affected by the quality of images at a specific time to be extracted, thus generally demonstrating accurate and practical land category feature extraction.

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References

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