A Machine Learning Approach to study the relationship between urban morphology and urban heat island
Project Description

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.

Supervisor
FUNG Jimmy Chi Hung
Quota
3
Course type
UROP1000
UROP1100
UROP2100
UROP3100
Applicant's Roles

The student needs to learn various machine learning techniques

Applicant's Learning Objectives

The project is expected to facilitate understanding the urban heat island effect and its mitigation strategies through urban design. The cross-comparison of the project’s outcome with numerical modeling results in the future would also be useful to consolidate the dynamic theory-based explanations.

Complexity of the project
Moderate