Urban Complexity through Vision Intelligence: Variance, Gradients, and Correlations across Six Italian Cities
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Esposti, Mirko Degli Bazzani, Armando Dellacasa, Chiara Falcioni, Matteo Massimon, Mario Pietropoli, Martino |
| author_facet | Esposti, Mirko Degli Bazzani, Armando Dellacasa, Chiara Falcioni, Matteo Massimon, Mario Pietropoli, Martino |
| contents | 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 Façade Degradation Score (FDS). The findings quantify Structural Heterogeneity (Spatial Variance), revealing significant quality dispersion (e.g., Milan $σ^2_{\mathrm{PCI}}=1.52$), 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 façade quality and greenery ($ρ\approx 0.35$), 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_09258 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Urban Complexity through Vision Intelligence: Variance, Gradients, and Correlations across Six Italian Cities Esposti, Mirko Degli Bazzani, Armando Dellacasa, Chiara Falcioni, Matteo Massimon, Mario Pietropoli, Martino Physics and Society Computers and Society 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 Façade Degradation Score (FDS). The findings quantify Structural Heterogeneity (Spatial Variance), revealing significant quality dispersion (e.g., Milan $σ^2_{\mathrm{PCI}}=1.52$), 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 façade quality and greenery ($ρ\approx 0.35$), 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. |
| title | Urban Complexity through Vision Intelligence: Variance, Gradients, and Correlations across Six Italian Cities |
| topic | Physics and Society Computers and Society |
| url | https://arxiv.org/abs/2511.09258 |