In this work, a flower-shaped shading system with integrated tracking photovoltaic, suitable for sustainable extensive urban coverages, is designed. Detailed photovoltaic energy yield simulations with a single-diode model approach are performed to disclose the potential of the proposed tracking photovoltaic shading device (PVSD). Simulations are performed with reference to a case study. A double-layer space truss is used to house the innovative modular photovoltaic tracking system, and the first application is envisaged for the coverage of a public market area of a sunny municipality in Southern Italy. By comparing it with the traditional photovoltaic fixed system, the results of the simulations show a steadier energy generation of the new PVSD, and it also provides better coverage with renewable energy during the hours of the day when the traditional system produces low electric energy. Lastly, an early interactive prototype of the PVSD system is presented. The tracking mechanism is carefully designed, 3D-printed at a small scale and tested with a motorized dynamic system controlled by a microcontroller board. The realization of the physical prototype and the engineering of the movement mechanism confirmed the feasibility and the correct functioning of the conceived system opening to real-scale applications.

The need to establish safe, accessible, and inclusive pedestrian routes is considered one of the European Union’s main priorities. We have developed a method of assessing pedestrian mobility in the surroundings of urban public buildings to evaluate the level of accessibility and inclusion, especially for people with reduced mobility. In the first stage of assessment, artificial intelligence algorithms were used to identify pedestrian crossings and the precise geographical location was determined by deep learning-based object detection with satellite or aerial orthoimagery. In the second stage, Geographic Information System techniques were used to create network models. This approach enabled the verification of the level of accessibility for wheelchair users in the selected study area and the identification of the most suitable route for wheelchair transit between two points of interest. The data obtained were verified using inertial sensors to corroborate the horizontal continuity of the routes. The study findings are of direct benefit to the users of these routes and are also valuable for the entities responsible for ensuring and maintaining the accessibility of pedestrian routes.

In recent years, automatic segmentation and classification of data from digital surveys have taken a central role in built heritage studies. However, the application of Machine and Deep Learning (ML and DL) techniques for semantic segmentation of point clouds is complex in the context of historic architecture because it is characterized by high geometric and semantic variability. Data quality, subjectivity in manual labeling, and difficulty in defining consistent categories may compromise the effectiveness and reproducibility of the results. This study analyzes the influence of three key factors—annotator specialization, point cloud density, and sensor type—in the supervised classification of architectural elements by applying the Random Forest (RF) algorithm to datasets related to the architectural typology of the Franciscan cloister. The main innovation of the study lies in the development of an advanced feature selection technique, based on multibeam statistical analysis and evaluation of the p-value of each feature with respect to the target classes. The procedure makes it possible to identify the optimal radius for each feature, maximizing separability between classes and reducing semantic ambiguities. The approach, entirely in Python, automates the process of feature extraction, selection, and application, improving semantic consistency and classification accuracy.

The inclusive use of urban spaces necessitates detailed knowledge of the accessibility of public buildings or places where educational, health or administrative services are provided. Despite the improvements already made in many cities regarding architectural work, further changes to public buildings and other spaces, such as old buildings or areas of historical importance, are still required. To study this problem, we developed a model based on photogrammetric techniques and the use of inertial and optical sensors. The model allowed us to perform a detailed analysis of urban routes in the surroundings of an administrative building, by means of mathematical analysis of pedestrian routes. It was applied to the specific case of people with reduced mobility and included analysis of the building accessibility as well as detection of suitable transit routes, deterioration of the road surfaces and the presence of architectural obstacles on the route.