Semantic Segmentation of Hazardous Facilities in Rural Area Using U-Net from KOMPSAT Ortho Mosaic Imagery

Sung Hyun Gong, Hyung Sup Jung, Moung Jin Lee, Kwang Jae Lee, Kwan Young Oh, Jae Young Chang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Rural areas, which account for about 90% of the country’s land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents’ lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents’ lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.

Original languageEnglish
Pages (from-to)1693-1705
Number of pages13
JournalKorean Journal of Remote Sensing
Volume39
Issue number6-3
DOIs
StatePublished - 2023

Keywords

  • Convolutional nerual network
  • Deep learning
  • Rural
  • Semantic segmentation
  • U-Net

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