Matteo Falcioni | Searcharxiv

arXiv2026

Watershed vs. Region Growing for Individual Tree Segmentation from Airborne LiDAR: An Urban Case Study in Bologna

We present a LiDAR-based pipeline for the segmentation and structural characterisation of tall urban vegetation in Bologna. Starting from airborne LiDAR point clouds previously classified as high vegetation by a Random Forest model, we implement and compare two individual-tree segmentation strategies: a watershed algorithm applied to the Canopy Height Model and a point-wise region growing algorithm operating directly on the three-dimensional cloud. Over an area of 12 tiles covering the Talea district, the watershed method detects a larger number of trees (7589), dominated by short trees with a height peak around 5 meters, whereas region growing yields fewer trees (6432) but retains the highest returns, yielding individuals above 40 meters, reflecting the absence of a smoothing step in the latter. We further confront the segmentation results with the municipal Open Data catalogue Alberi in manutenzione: on a sample tile roughly half of the LiDAR-detected trees turn out to be absent from the catalogue, several catalogued positions correspond to locations where no tree is observed, and the majority of the height records date back about two decades, which makes the dataset unsuitable as a ground truth reference. From the segmented trees we extract height and crown radius and use them to compute preliminary vegetation indicators, namely above-ground biomass and carbon storage through allometric relations and annual pollen production through species-specific inflorescence coefficients. All results are collected in an interactive per-tree metadata map. The pipeline is modular and can be recalibrated as improved classifications, field campaigns, or complementary sensing technologies become available, providing a scalable basis for data-driven urban greenery planning.

physics.app-ph↗

arXiv2025

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

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.

physics.soc-ph↗