The identification and recognition of urban features are essential for creating accurate and comprehensive digital representations of cities. In particular, the automatic characterization of façade elements plays a key role in enabling semantic enrichment and 3D reconstruction. It also supports urban analysis and underpins various applications, including planning, simulation, and visualization. This work presents a pipeline for the automatic recognition of façades within complex urban scenes represented as point clouds. The method employs an enhanced partitioning strategy that extends beyond strict building footprints by incorporating surrounding buffer zones, allowing for a more complete capture of façade geometry, particularly in dense urban contexts. This is combined with a primitive recognition stage based on the Hough transform, enabling the detection of both planar and curved façade structures. The proposed partitioning overcomes the limitations of traditional footprint-based segmentation, which often disregards contextual geometry and leads to misclassifications at building boundaries. Integrated with the primitive recognition step, the resulting pipeline is robust to noise and incomplete data, and supports geometry-aware façade recognition, contributing to scalable analysis of large-scale urban environments.

Aerial LiDAR (and photogrammetric) surveys are becoming a common practice in land and urban management, and aerial point clouds (or the reconstructed surfaces) are increasingly used as digital representations of natural and built structures for the monitoring and simulation of urban processes or the generation of what-if scenarios. The geometric analysis of a “digital twin” of the built environment can contribute to provide quantitative evidence to support urban policies like planning of interventions and incentives for the transition to renewable energy. In this work, we present a geometry-based approach to efficiently and accurately estimate the photovoltaic (PV) energy produced by urban roofs. The method combines a primitive fitting technique for detecting and characterizing building roof components from aerial LiDAR data with an optimization strategy to determine the maximum number and optimal placement of PV modules on each roof surface. The energy production of the PV system on each building over a specified time period (e.g., one year) is estimated based on the solar radiation received by each PV module and the shadow projected by neighboring buildings or trees and efficiency requirements. The strength of the proposed approach is its ability to combine computational techniques, domain expertise, and heterogeneous data into a logical and automated workflow, whose effectiveness is evaluated and tested on a large-scale, real-world urban areas with complex morphology in Italy.

Modelling objects at a large resolution or scale brings challenges in the storage and processing of data and requires efficient structures. In the context of modelling urban environments, we face both issues: 3D data from acquisition extends at geographic scale, and digitization of buildings of historical value can be particularly dense. Therefore, it is crucial to exploit the point cloud derived from acquisition as much as possible, before (or alongside) deriving other representations (e.g., surface or volume meshes) for further needs (e.g., visualization, simulation). In this paper, we present our work in processing 3D data of urban areas towards the generation of a semantic model for a city digital twin. Specifically, we focus on the recognition of shape primitives (e.g., planes, cylinders, spheres) in point clouds representing urban scenes, with the main application being the semantic segmentation into walls, roofs, streets, domes, vaults, arches, and so on. Here, we extend the conference contribution in Romanengo et al. (2023a), where we presented our preliminary results on single buildings. In this extended version, we generalize the approach to manage whole cities by preliminarily splitting the point cloud building-wise and streamlining the pipeline. We added a thorough experimentation with a benchmark dataset from the city of Tallinn (47,000 buildings), a portion of Vaihingen (170 building) and our case studies in Catania and Matera, Italy (4 high-resolution buildings). Results show that our approach successfully deals with point clouds of considerable size, either surveyed at high resolution or covering wide areas. In both cases, it proves robust to input noise and outliers but sensitive to uneven sampling density. • Processing 3D point clouds of urban areas to generate a semantic model for a city digital twin. • Recognizing shape primitives (planes, cylinders, spheres) in large-scale urban point clouds. • Semantic segmentation of building point clouds into facades, roofs, and pavement. • Experimenting on real datasets (Vaihingen, Tallinn, Catania and Matera)

Users frequently seek to fabricate objects whose outer surfaces consist of regions with different surface attributes, such as color or material. Manufacturing such objects in a single piece is often challenging or even impossible. The alternative is to partition them into single-attribute volumetric parts that can be fabricated separately and then assembled to form the target object. Facilitating this approach requires partitioning the input model into parts that conform to the surface segmentation and that can be moved apart with no collisions. We propose Surface2Volume , a partition algorithm capable of producing such assemblable parts, each of which is affiliated with a single attribute, the outer surface of whose assembly conforms to the input surface geometry and segmentation. In computing the partition we strictly enforce conformity with surface segmentation and assemblability, and optimize for ease of fabrication by minimizing part count, promoting part simplicity, and simplifying assembly sequencing. We note that computing the desired partition requires solving for three types of variables: per-part assembly trajectories, partition topology, i.e. the connectivity of the interface surfaces separating the different parts, and the geometry, or location, of these interfaces. We efficiently produce the desired partitions by addressing one type of variables at a time: first computing the assembly trajectories, then determining interface topology, and finally computing interface locations that allow parts assemblability. We algorithmically identify inputs that necessitate sequential assembly, and partition these inputs gradually by computing and disassembling a subset of assemblable parts at a time. We demonstrate our method's robustness and versatility by employing it to partition a range of models with complex surface segmentations into assemblable parts. We further validate our framework via output fabrication and comparisons to alternative partition techniques.