| Title |
Heatwave Vulnerability Analysis of Construction Sites Using Satellite Imagery Data and Deep Learning |
| Authors |
김슬기(Kim, Seulgi) ; 박승희(Park, Seunghee) |
| DOI |
https://doi.org/10.12652/Ksce.2022.42.2.0263 |
| Keywords |
폭염; 도시열섬현상; 건설재난; 인공위성영상; 딥러닝 Heat waves; Urban heat island; Construction disaster; Satellite imagery; Deep learning |
| 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 °C, 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. |