Local Climate Zone Mapping Using Clustering Algorithms: A Case Study of Dhaka, Bangladesh

Dhaka, a fast-growing metropolis with a population of approximately 21.7 million, is going through rapid social and economic advancement. However, rapid unplanned urbanization has resulted in substantial environmental damage and poses a severe public health risk. The Local Climate Zone (LCZ), a novel concept for urban heat island research and an-alyzing urban morphology, provides a standardized categorization system for the urban environment. This study aims to investigate the LCZ mapping of two densely populated Asian cities: Dhaka, Bangladesh, and Kolkata, India, using two machine learning clustering algorithms, K-means and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). The research primarily focuses on Dhaka, but Kolkata is chosen as a comparative city to deliver a more in-depth analysis because the two cities share similar geographical and environmental characteristics. The study reveals that ISODATA is particularly good at classifying specific urban types, especially distinguishing between open and compact mid-rise.