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