Urban heat island is a commonly observed phenomenon and an actively researched topic. One of the contributors to urban heat islands is the absorption and trapping of heat from complex three-dimensional building structures in megacities. Studies have been looking at the relationship between satellite observed land surface temperature and the type of land use/urban category data. However, the complex three-dimensional building information is always simplified in those datasets, whereas in-situ measurement is always limited in spatial coverage, hindering the study on regional impact. In this project, open-source machine learning algorithms will be used to study the relationship between the urban surface temperature observed from satellite images and the three-dimensional urban structure in the greater bay area. The machine learning model would be trained to estimate the land surface temperature and compared to the one estimated by regression analysis.
The student needs to learn various machine learning techniques
The project is expected to facilitate understanding the urban heat island effect and its mitigation strategies through urban design. The cross-comparison of the projects outcome with numerical modeling results in the future would also be useful to consolidate the dynamic theory-based explanations.