Simulations of urban transformations are an effective tool for engaging citizens and enhancing their understanding of urban design outcomes. Citizens’ involvement can positively contribute to foster resilience for mitigating the impact of climate change. Successful integration of Nature-Based Solutions (NBS) into the urban fabric enables both the mitigation of climate hazards and positive reactions of citizens. This paper presents two case studies in a southern district of Milan (Italy), investigating the emotional reaction of citizens to existing urban greenery and designed NBS. During the events, the participants explored in Virtual Reality (VR) (n = 48) and Augmented Reality (AR) (n = 63) (i) the district in its current condition and (ii) the design project of a future transformation including NBS. The environmental exploration and the data collection took place through the exp-EIA© method, integrated into the mobile app City Sense. The correlations between the color features of the viewed landscape and the emotional reaction of participants showed that weighted saturation of green and lime colors reduced the unpleasantness both in VR and AR, while the lime pixel area (%) reduced the unpleasantness only in VR. No effects were observed on the Arousal and Sleepiness factors. The effects show high reliability between VR and AR for some of the variables. Implications of the method and the benefits for urban simulation and participatory processes are discussed.
Visual post-occupancy evaluation of a restorative garden using virtual reality photography: Restoration, emotions, and behavior in older and younger people
Natural environments have a restorative effect from mental/attentional fatigue, prevent stress, and help to revitalize psychological and physical resources. These benefits are crucial for promoting active aging, which is particularly relevant given the phenomenon of population aging in recent decades. To be considered restorative, green spaces have to meet specific requirements in ecological and psychological terms that can be assessed through Post-Occupancy Evaluation (POE), a multimethod approach commonly used by environmental psychologists and landscape architects after construction to evaluate the design outcomes from the users’ perspective. Generally, POEs consist of surveys and/or interviews accompanied by more or less structured observations of onsite users’ behavior. Despite this, various practical constraints can prevent physical access to the renovated area (e.g., weather conditions, time/resources limits, health issues, bureaucratic constraints). Exploiting digital tools for such an assessment can be a crucial support in such circumstances. The current study presents the visual POE of a restorative garden for older adults in Milan, Italy. We developed a web application, that includes the exp-EIA© patented method, which allows participants to virtually explore a visual simulation of the environment and provide their feedback. We identified 3 representative viewpoints in the redeveloped garden differing from each other for the functions and the design principles that inspired the transformation. For each point of view, we created 360° Virtual Reality photographs, that can be navigated by looking around, i.e., panning, from the standing point of each view. In connection to each virtual scene, a survey was conducted (N = 321). The focus was the psychological experience related to each viewpoint, assessed with two psychometric scales investigating the constructs of emotions (pleasure and arousal) and restoration (fascination, being away, coherence, scope, and environmental preference); such information is integrated with behavioral aspects, including the main activities prefigured by participants and their visual exploration of the VR photography. The results of the virtual exploration show that the garden is perceived as restorative, with a more intense effect in a spot purposely designed. The emotions experienced in the garden are positive and a mild level of arousal is observed. The behavioral dimension is characterized by predominantly contemplative activities and contact with nature. A cartographic representation of the psychological and behavioral data is developed, to support the maintenance of the garden.
A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification
The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution.
Visual Programming for a Machine Semi-Automatic Process of HBIM Models Geometric Evaluation
The topic of the relationship between digital restitutive model and measurement can find important development possibilities in machine procedures, in particular in the Historic Building Information Modeling (HBIM) field. In fact, BIM uses parameterized and pre-defined objects in special 3D libraries articulated according to the architectural components, not corresponding to ideal configurations. Moreover, BIM platforms are limited in modeling deformations, damages, and degradations. The paper investigates the advantages of using visual programming to increase the possibilities given by the BIM software, born for new buildings, by carrying out a semi-automatic assessment of the geometric reliability directly in the BIM environment. In particular, the algorithm compares model’s shapes with the cast of the artifact given by the point cloud, and declares it, by automatically filling a dedicated reliability parameter linked to the BIM model element.
A Proposal of Integration of Point Cloud Semantization and VPL for Architectural Heritage Parametric Modeling
Current architectural survey processes utilize point clouds generated by laser scanning and digital photogrammetry. Increasingly, these surveys produce 3D models, particularly parametric models, in what is known as the “scan to 3D model” or “scan to BIM” process. However, the phases of analysis and classification of architectural elements, segmentation and semantization of point clouds, and semi-automatic modeling remain complex and labor-intensive and require an active role commitment of the scholar or modeler. These steps are usually performed manually, resulting in high subjectivity and low reproducibility. This paper proposes a reproducible workflow that automatically segments point clouds, identifies geometric shapes by comparing them with a library of ideal geometries, and extracts necessary points for modeling through mathematical analysis. The extracted information is then processed using a visual programming algorithm, imported into the VPL environment, and used for automated modeling. Initial results from an ongoing experiment on the automated modeling of vaults using point clouds from surveys are presented.