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This study assesses the performance of a global Local Climate Zone (LCZ)product. We examined the built-type classes of LCZs in three major metropolitanareas within the U.S. A reference LCZ was constructed using a simple rule-basedmethod based on high-resolution 3D building maps. Our evaluation demonstratedthat the global LCZ product struggles to differentiate classes that demandprecise building footprint information (Classes 6 and 9), and classes thatnecessitate the identification of subtle differences in building elevation(Classes 4-6). Additionally, we identified inconsistent tendencies, where thedistribution of classes skews differently across different cities, suggestingthe presence of a data distribution shift problem in the machine learning-basedLCZ classifier. Our findings shed light on the uncertainties in global LCZmaps, help identify the LCZ classes that are the most challenging todistinguish, and offer insight into future plans for LCZ development andvalidation.

Assessment of Local Climate Zone Products via Simplified Classification Rule with 3D Building Maps