대한토목학회논문집 (KSCE Journal of Civil and Environmental Engineering Research)

  • 제42권2호
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  • Pages.263-272
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  • 2022
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  • 1015-6348(pISSN)
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  • 2799-9629(eISSN)
대한토목학회 (Korean Society of Civil Engeneers)
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인공위성영상과 딥러닝을 이용한 건설공사현장 폭염취약지역 분석

Heatwave Vulnerability Analysis of Construction Sites Using Satellite Imagery Data and Deep Learning

  • 김슬기 (성균관대학교 미래도시융합공학과) ;
  • 박승희 (성균관대학교 건설환경공학부)
  • 투고 : 2021.12.06
  • 심사 : 2021.12.30
  • 발행 : 2022.04.01
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초록

폭염과 도시열섬현상은 기후변화가 진행됨에 따라 피해가 더욱 커지고 있으며, 2050년까지 폭염 발생빈도는 2~6배가 증가될 것으로 예측된다. 특히, 폭염기간동안 건설공사현장에서의 근로자가 느끼는 더위체감지수는 매우 높으며, 도시열섬현상까지 고려하게 되면 체감지수는 더욱 높아진다. 열에 취약한 건설현장 환경과 건설근로자의 상황은 나아지지 않고 있으며, 피해를 줄이기 위해서는 효과적인 대응이 필요한 시점이다. 본 연구에서는 인공위성영상 이미지와 Land Surface Temperature (LST)와 Long Short Term Memory (LSTM) 딥러닝 모델 기법을 적용하여 33℃ 이상 온도가 되는 지역을 분석하고, 폭염에 취약한 건설공사현장을 식별하여 폭염 및 도시열섬현상의 복합적인 피해를 가중시킬 수 있는 가장 취약한 지역을 예측하여 도출하였다. 예측 결과를 통해 건설근로자의 안전을 보장하고, 건설현장 경보시스템의 기반이 될 수 있기를 기대한다.

As a result of climate change, the heatwave and urban heat island phenomena have become more common, and the frequency of heatwaves is expected to increase by two to six times by the year 2050. In particular, the heat sensation index felt by workers at construction sites during a heatwave is very high, and the sensation index becomes even higher if the urban heat island phenomenon is considered. The construction site environment and the situations of construction workers vulnerable to heat are not improving, and it is now imperative to respond effectively to reduce such damage. In this study, satellite imagery, land surface temperatures (LST), and long short-term memory (LSTM) were applied to analyze areas above 33 ℃, with the most vulnerable areas with increased synergistic damage from heat waves and the urban heat island phenomena then predicted. It is expected that the prediction results will ensure the safety of construction workers and will serve as the basis for a construction site early-warning system.

키워드

과제정보

본 연구는 행정안전부 극한재난대응기반기술개발사업(2019-MOIS31-011) 및 국토교통부의 스마트시티 혁신인재육성사업으로 지원되었습니다. 본 논문은 2021 CONVENTION 논문을 수정·보완하여 작성되었습니다.

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