Improving the local climate zone classification with building height, imperviousness, and machine learning for urban models

Improving the local climate zone classification with building height, imperviousness, and machine learning for urban models

Abstract The Local Climate Zone (LCZ) classification is already widely used in urban heat island and other climate studies. The current classification method does not incorporate crucial urban auxiliary GIS data on building height and imperviousness that could significantly improve urban-type LCZ cl...

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Bibliographic Details
Main Authors: Kwun Yip Fung, Zong-Liang Yang, Dev Niyogi
Format: Article
Language:English
Published: Springer 2022-06-01
Series:Computational Urban Science
Subjects:
Local Climate Zone
Machine Learning
Deep Learning
Urban Classification
Online Access:https://doi.org/10.1007/s43762-022-00046-x

Internet

https://doi.org/10.1007/s43762-022-00046-x

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