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http://dx.doi.org/10.12652/Ksce.2022.42.2.0263

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

Kim, Seulgi (Sungkyunkwan University)
Park, Seunghee (Sungkyunkwan University)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.42, no.2, 2022 , pp. 263-272 More about this Journal
Abstract
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.
Keywords
Heat waves; Urban heat island; Construction disaster; Satellite imagery; Deep learning;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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1 Meng, X., Cheng, J., Zhao, S., Liu, S. and Yao, Y. (2019). "Estimating land surface temperature from Landsat-8 data using the NOAA JPSS enterprise algorithm."Remote Sensing, Vol. 11, pp. 155-172.   DOI
2 United States Geological Survey (USGS) (2019). Landsat 8 (l8) data users handbook, Department of the Interior U.S. Geological Survey, LSDS-1574, Version 5.0.
3 Kim, B. C., Kang, J. W., Park, C. and Kim, H. J. (2020a). "Analysis of urban heat island (UHI) alleviating effect of urban parks and green space in Seoul using deep neural network (DNN) model." Journal of the Korean Institute of Landscape Architecture, Vol. 48, No. 4, pp. 19-28 (in Korean).   DOI
4 Kim, J. S., Lee, D. G., Sung, S. Y., Jeong, S. G. and Park, J. H. (2015). "Study of vulnerable district characteristics on urban heat island according to land use using normalized index." Journal of Korea Planning Association, Vol. 50, No. 5, pp. 59-72 (in Korean).   DOI
5 Kim, Y. H., Oh, I. B., Lee, J. H., Kim, J. H., Chung, I. S., Lim, H. J., Park, J. K. and Park, J. S. (2016). "Evaluation of heat stress and comparison of heat stress indices in outdoor work." Journal of Environmental Health Sciences, Vol. 42, No. 2, pp. 85-91 (in Korean).   DOI
6 Vanhellemont, Q. (2020). "Combined land surface emissivity and temperature estimation from Landsat 8 OLI and TIRS." ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 166, pp. 390-402.   DOI
7 Ko, Y. J. and Cho, K. H. (2020). "Analysis of areas vulnerable to urban heat island using hotspot analysis -a case study in Jeonju city, Jeollabuk-do-." Journal of the Korean Institute of Landscape Architecture, Vol. 48, No. 5, pp. 67-79 (in Korean).   DOI
8 Lee, D. G., Lee, M. H., Kim, B. E., Yu, J. H., Oh, Y. J. and Park, J. I. (2020). "A study for estimation of high resolution temperature using satellite imagery and machine learning models during heat waves." Korean Journal of Remote Sensing, Vol. 36, No. 5-4, pp. 1179-1194 (in Korean).   DOI
9 Varghese, B. M., Hansen, A., Nitschke, M., Nairn, J., HansonEasey, S., Bi, P. and Pisaniello, D. (2019). "Heatwave and work-related injuries and illnesses in adelaide, australia: A case-crossover analysis using the Excess Heat Factor (EHF) as a universal heatwave index." International Archives of Occupational and Environmental Health, Vol. 92, No. 2, pp. 263-272.   DOI
10 Founda, D. and Santamouris, M. (2017). "Synergies between urban heat island and heat waves in Athens (Greece), during an extremely hot summer (2012)." Scientific Reports, Vol. 7, pp. 10973.   DOI
11 Zhao, L., Oppenheimer, M., Zhu, Q., Baldwin, J. W., Ebi, K. L., Bou-Zeid, E., Guan, K. and Liu, X. (2018). "Interactions between urban heat islands and heat waves." Environmental Research Letters, Vol. 13, No. 3, pp. 034003.   DOI
12 Kim, S. Y., Lee, S. J. and Lee, Y. W. (2020c). "Retrieval of land surface temperature using landsat 8 images with deep neural networks." Korean Journal of Remote Sensing, Vol. 36, No. 3, pp. 487-501 (in Korean).   DOI
13 Kim, D. H. and Lee, J. B. (2020). "Spatial changes in work capacity for occupations vulnerable to heat stress: Potential regional impacts from global climate change." Safety and Health at Work, Vol. 11, No. 1, pp. 1-9.   DOI
14 Kim, D. W., Chung, J. H., Lee, J. S. and Lee, J. S. (2014). "Characteristics of heat wave mortality in Korea." Atmosphere, Vol. 24, No. 2, pp. 225-234 (in Korean).   DOI
15 Kim, Y. I., Kim, D. H. and Lee, S. O. (2020b). "Prediction of temperature and heat wave occurrence for summer season using machine learning."Journal of Korean Society for Disaster and Security, Vol. 13, No. 2, pp. 27-38 (in Korean).   DOI
16 Korea Meteorological Administration (KMA) (2018). Newsletter abnormal climate monitoring, No. 11-1360000-000072-08, Korea Meteorological Administration, Seoul, pp. 1-2 (in Korean).
17 Korea Meteorological Administration (KMA) (2021). Available at: https://data.kma.go.kr (Accessed: October 3, 2021).