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

TL;DR

This paper evaluates the accuracy of a global Local Climate Zone product in U.S. cities, highlighting its difficulties in classifying certain built types and elevations, and discusses data distribution issues affecting its performance.

Contribution

It introduces a simple rule-based reference LCZ classification using 3D building maps and assesses the global LCZ product's limitations across different urban environments.

Findings

Global LCZ struggles with classes requiring detailed building footprint data.

Inconsistent class distribution across cities indicates data shift issues.

Certain classes are more challenging to distinguish due to subtle elevation differences.

Abstract

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.