Assessment of Local Climate Zone Products via Simplified Classification Rule with 3D Building Maps
It highlights uncertainties in global LCZ maps for urban climate researchers, identifying challenging classes and data distribution shifts, which is incremental for validation efforts.
This study assessed a global Local Climate Zone (LCZ) product by comparing it to a reference built from high-resolution 3D building maps in three U.S. metropolitan areas, finding that it struggles to differentiate specific classes (e.g., Classes 6 and 9) and shows inconsistent distributions across cities.
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.