Automatic Building Extraction Using SpaceNet Building Dataset and Context-based ResU-Net

Suhong Yoo, Cheol Hwan Kim, Youngmok Kwon, Wonjun Choi, Hong Gyoo Sohn

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Building information is essential for various urban spatial analyses. For this reason, continuous building monitoring is required, but it is a subject with many practical difficulties. To this end, research is being conducted to extract buildings from satellite images that can be continuously observed over a wide area. Recently, deep learning-based semantic segmentation techniques have been used. In this study, a part of the structure of the context-based ResU-Net was modified, and training was conducted to automatically extract a building from a 30 cm Worldview-3 RGB image using SpaceNet's building v2 free open data. As a result of the classification accuracy evaluation, the f1-score, which was higher than the classification accuracy of the 2nd SpaceNet competition winners. Therefore, if Worldview-3 satellite imagery can be continuously provided, it will be possible to use the building extraction results of this study to generate an automatic model of building around the world.

Original languageEnglish
Pages (from-to)685-694
Number of pages10
JournalKorean Journal of Remote Sensing
Issue number5-2
Publication statusPublished - 2022 Oct

Bibliographical note

Publisher Copyright:
© 2022 Korean Society of Remote Sensing. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Engineering (miscellaneous)
  • Computers in Earth Sciences
  • Earth and Planetary Sciences (miscellaneous)


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