Urban Complexity through Vision Intelligence: Variance, Gradients, and Correlations across Six Italian Cities

TL;DR

This study develops a scalable vision-based methodology to analyze urban quality metrics across six Italian cities, revealing significant heterogeneity, weak urban gradients, and modest correlations among visual environment features.

Contribution

It introduces an advanced Urban Vision Intelligence system for objective, large-scale analysis of urban visual quality and structure across multiple cities.

Findings

Significant spatial variance in urban quality metrics.

Weak correlation between distance from city center and quality.

Positive association between façade quality and greenery.

Abstract

This paper introduces a scalable methodology for the objective analysis of quality metrics across six major Italian metropolitan areas: Rome, Bologna, Florence, Milan, Naples, and Palermo. Leveraging georeferenced Street View imagery and an advanced Urban Vision Intelligence system, we systematically classify the visual environment, focusing on key metrics such as the Pavement Condition Index (PCI) and the Fa\c{c}ade Degradation Score (FDS). The findings quantify Structural Heterogeneity (Spatial Variance), revealing significant quality dispersion (e.g., Milan $\sigma^2_{\mathrm{PCI}}=1.52$), and confirm that the classical Urban Gradient -- quality variation as a function of distance from the core -- is consistently weak across all sampled cities ($R^2 < 0.03$), suggesting a complex, polycentric, and fragmented morphology. In addition, a Cross-Metric Correlation Analysis highlights stable but modest interdependencies among visual dimensions, most notably a consistent positive association between fa\c{c}ade quality and greenery ($\rho \approx 0.35$), demonstrating that structural and contextual urban qualities co-vary in weak yet interpretable ways. Together, these results underscore the diagnostic potential of Vision Intelligence for capturing the integrated spatial and morphological structure of Italian cities and motivate a large national-scale analysis.