Data-driven Urban Surface Classification Elucidates Global City Heterogeneity

TL;DR

This paper introduces a global, data-driven urban surface classification framework called DUEZ, which improves the physical detail and consistency of urban heterogeneity representation for environmental modeling.

Contribution

The authors developed a unified, unsupervised clustering approach to classify global urban surfaces into 27 DUEZs, capturing fine-scale urban heterogeneity and regional differences.

Findings

DUEZ classifies 85% of the global population's urban surfaces.

Compared to the Local Climate Zone scheme, DUEZ offers more detailed urban form representation.

Identified nine predominant urban textures with regional and socioeconomic relevance.

Abstract

Accurate urban surface characterization is essential for environmental modeling, risk assessment, and climate adaptation. However, existing classifications of urban surfaces lack the global consistency and physical detail to fully represent present-day urban heterogeneity. To address this need, we developed a globally unified, Data-driven Urban Environmental Zone (DUEZ) framework. By applying unsupervised clustering to high-resolution (500-m) datasets of building morphology, vegetation, and surface imperviousness, we classified global urban surfaces into 27 DUEZs, representing the exposure setting for approximately 85% of the global population. Compared to the Local Climate Zone scheme, DUEZ framework provides a more detailed representation of urban form, capturing the fine-scale mixing of built and vegetated surfaces in modern cities. Further aggregation of DUEZ patterns revealed nine predominant urban textures globally with regional differences and socioeconomic relevance. The DUEZ framework enhances physical representation of complex urban surfaces in numerical models and establishes a consistent, data-driven basis for global urban environmental studies.