Understanding subjective urban experiences is essential for designing cities that enhance well-being. Urban design should account for the psychological effects of environments on individuals, as these significantly shape perceptions and behaviors. However, a major challenge is the limited availability of urban perception data. Recent studies have leveraged large, crowdsourced datasets like Place Pulse 2.0 (PP2) to inform machine learning (ML) models for urban perception prediction, but the accuracy and reliability of outcomes remain underexplored. There is a critical need to evaluate whether these datasets truly capture human perceptions. This study investigates the role of urban street images in understanding environmental perceptions, using the PP2 dataset and ML techniques. It explores various ML pipelines, employing TPot AutoML for model selection and 5-fold cross-validation to prevent overfitting. The goal is to identify the most efficient model that strengthens the link between automated predictions and human perception. The study also applies SHAP (SHapley Additive exPlanations) to interpret model outputs, revealing feature importance and interactions. This improves transparency and ensures ML-generated insights are actionable for urban planning. By rigorously testing ML pipelines, this research enhances predictive accuracy and contributes to the development of reliable urban design tools. The findings highlight ML’s potential in processing large-scale perception data, uncovering hidden patterns, and informing people-centered urban planning. However, further validation against real-world surveys is necessary to ensure robustness and generalizability in assessing urban perceptions.
Strategic Classification of the Integration Between Artificial Intelligence (AI) and Building Information Modelling (BIM): Opportunities and Future Challenges
The integration of Artificial Intelligence (AI) and Building Information Modelling (BIM) represents a promising but complex frontier in the construction industry. While BIM has already transformed construction workflows through digitalization and lifecycle management, AI has the potential to further advance automation, optimization, and data-driven decision-making. This study aims to provide a systematic classification of AI applications within BIM, identifying four key categories: AI as a digital consultant, collaborative interface, process regulator, and process outcome. Through a scientometric analysis of literature and structured mapping of existing software, the paper evaluates the state of the art, exploring the capabilities and limitations of current solutions. Results highlight how AI + BIM tools are transforming various lifecycle stages, from conceptual design to construction and operations. However, challenges remain, including the lack of standardization, risks related to data security, and the balance between automation and human oversight. This classification framework not only structures existing knowledge but also directs future research and applications, encouraging critical reflection on AI’s role in advancing BIM methodologies while considering its implications for transparency, efficiency, and technological governance.
Artificial Creativity. Design Evolution in the Age of AI
The advent of artificial intelligence (AI), in combination with the ubiquity of digital sensors, computer networks, and automation that has characterised the last decade, is transforming the socio-economic environment and defining a probable new industrial era. This evolution also inevitably involves the world of Design by redefining the designer’s role, the object of design, and user relationships. Many of today’s artefacts, whether an iPhone application, a car, or a building, are increasingly connected to the designer who conceived them, thanks to a continuous flow of data detailing many aspects of the user experience. This same information can be used to train AI neural networks capable of autonomously generating specific solutions without human intermediation. An artificial intelligence engine can thus anticipate users’ needs and behaviours, proposing solutions that are improved and customised according to the particular use that distinguishes each customer. This paradigm shift has important implications for the role of the designer. Through the analysis of pioneering case studies, this paper analyses the possible developments, delving into the repercussions for design theory and practice concerning the theoretical framework used today to interpret the discipline of Design.
AI-Enhanced UAV Photogrammetry Point Clouds for Cultural Heritage Assessment
Advancements in low-altitude remote sensing and image analysis have revolutionized the digitisation of real-world objects, initially represented as point clouds. Over the past decade, drone-based surveying has gained traction; however, noise during data capture and 3D reconstruction remains a critical challenge, affecting the accuracy and usability of UAV images in real-world applications. This study presents a novel approach to enhance cultural heritage assessment using deep learning models based on the Autoencoder Gaussian Mixture Clustering Model and the Autoencoder K-means Clustering Model. These models improve the accuracy of point clouds generated from UAV images for better condition assessment and survey. The research focuses on photogrammetric accuracy parameters for clustering point clouds. The new approach chooses K-means for finding global patterns due to its robust accuracy, and the Gaussian Mixture Clustering Model for local changes and inspection applications. Moreover, these models investigate the accuracy of point cloud clusters generated from K-means and the Gaussian Mixture Clustering Model. To validate the new approach, the study evaluates variations in key parameters under diverse built-environment conditions using the Temple of Neptune point cloud in Paestum, Italy. The results demonstrate significant improvements in point cloud 3D reconstruction, leading to more accurate surveys and assessments. The proposed method surpasses traditional techniques, offering enhanced applicability for point-cloud filtering. Furthermore, comparisons of clustering results highlight the algorithm, establishing its promising potential for advancing UAV-based surveying and inspection practices.
Semi-Automated Feature Recognition and Localization in 3D Point Cloud by Using AI
The application of Artificial Intelligence (AI) in Cultural Heritage surveying has gained notable interest, particularly in enhancing the classification and segmentation of 3D point clouds. Recent research focuses on automating processes to manage surveying data and integrating it into the BIM environment. The present research proposes a semi-automated AI-based pipeline to semantically classify architectural features in 3D point clouds and match them with an existing BIM library. The case study examines the school and theatre of the Crespi d’Adda industrial village, a UNESCO Cultural Heritage site. Data includes a 3D point cloud generated via terrestrial photogrammetry, with windows selected as the feature to model in the BIM library. The methodology encompasses three phases: (i) creating a Crespi d’Adda window dataset aligning with BIM library parameters; (ii) applying Machine Learning classifiers for semantic categorization; and (iii) using prediction algorithms to recognize windows in the point cloud, matching them to the BIM library, and calculating accuracy. This research bridges architectural representation and data mining, streamlining BIM reconstruction. Anticipated results include automated detection and labeling of elements with accurate placement in the BIM environment, enhancing efficiency and interdisciplinary integration.
From Temple to Church: The Evolution of San Lorenzo in Miranda Through Machine Learning
The paper addresses the challenges related to the application of machine learning solutions to support historical and architectural critical interpretation. The case study reported here is the complex of San Lorenzo in Miranda, located in the Roman Forum along the Via Sacra. The structure, originally conceived as a temple and later transformed into a church, is a multi-layered architectural palimpsest in which each construction phase, at least since Roman times, has inevitably influenced the subsequent modifications. The building’s rebirth upon itself through the integration and modification of its older portions is its main characteristic, imparting a specific complexity and interest. The first phase of the research focused on bibliographic study and 3D digital survey, both of which contributed to identifying the multiple construction phases of the building. In particular, the digital survey was implemented through two survey campaigns. The first involved a massive 3D laser scanner acquisition and UAV photogrammetric capturing, while the second integrated a topographic survey to georeference the captured data. The second phase concerned data interpretation, focusing on research questions related to a hypothetical reconstruction of the cella’s original wall covering layer. This question was addressed by leveraging machine learning algorithms used to automatically identify the covering traces.
Automated Recognition and Segmentation of Bricks Wall in Sicilian Monasteries
The oriental architecture present in the north-eastern cusp of Sicily is an expression of a mixed-race culture. This hybridization is evident in religious architecture. These buildings have the typological imprint of Catholic churches with Latin cross plans and towered facades, but preserve Arabic traces in the structure of the domes and connections, while also exhibiting Eastern Byzantine traditions in their masonry and rich decorations. The objective of this research is to automate the process of recognizing and segmenting bricks in wall structures to support the analysis of wall fabrics, a crucial task in archaeology and architectural restoration. Our approach processes a point cloud extracted from a facade to identify the wall texture. The results of segmentation can provide statistical information, documenting average brick size, mortar thickness, identification of homogeneous areas, and recognition of masonry sections built with different bricks. Alongside the numerical and abstract information, it is possible to identify the standard morphology of the brick, which also constitutes a sort of digital fingerprint of the church. The size of the bricks influences the geometry and layout of religious architecture. For example, the lateral facades are decorated with intertwined arches entirely composed of bricks. The spacing of the arches, their regularity, and the overall morphology are determined by the size and arrangement of the bricks. Bricks placed on the beds in different ways (stretcher, rowlock stretchers, or heading) determine the repetition or alteration of the geometric-formal modules reiterated in the elevations.
The Influence of Data Quality in Supervised ML-AI Classification Approaches for Historical Heritage
In recent years, the automatic segmentation and classification of digital survey data has been experimented with in built heritage studies. Despite the encouraging progress in the use of Machine and Deep Learning techniques, the semantic segmentation of point clouds is more complex, especially for the historic environment for which, due to the heterogeneity of shapes, it is more difficult to recognize homogeneous regions with similar properties. Given the need to process a large volume of already annotated data for the training and recognition of new scenes, the type and quality of the initial data play a fundamental role in the classification process, as they influence the subdivision into predefined categories that are not always consistent with a decomposition into architectural elements and sub-elements shared by the scientific community. This is an interpretative problem that already emerges from traditional manual labelling, which, being highly subjective, reduces the reproducibility of the results. The paper focuses on understanding to what extent the recognition of homogeneous regions is influenced by factors such as: manual labelling carried out by annotators with different specializations; density value of the point clouds; and type of data acquired depending on the acquisition sensor. These evaluations were conducted by employing the Random Forest algorithm on specific pre-processed point cloud datasets, with reference to the typology of the Franciscan cloister, in order to make the recognition flows more controlled and less ambiguous, aiming at an advancement towards more efficient modelling and management of the existing architectural heritage.
The Artifice Among Languages: Automating Geometric Processes Through AI
This paper explores the application of Artificial Intelligence (AI) in design and geometric analysis processes in the CAD environment, with reference to Descriptive Geometry. Although the use of AI in academia is constantly growing, the implementation of artificial intelligence tools for solving geometric problems remains limited. In this context, this research proposes an experimental approach to automate the creation and manipulation of NURBS geometric entities, focusing on the ellipse as a case study. This document details the process of converting curves generated by the ‘Archimedes Compass’ in a 3D space into NURBS ellipses. The conversion is achieved through programming in Python and facilitated by ChatGPT. The analysis shows how the mathematical exactness typical of NURBS may not always be compatible with the requirements of certain geometric procedures, making integration with dedicated algorithms and AI tools necessary. In a broader perspective, the work shows the potential of textual programming and AI in simplifying and generalising complex processes, enabling new levels of precision and flexibility in virtual modelling. Finally, the results obtained and possible future perspectives in different areas of drawing and geometric representation are discussed.
Impact of Varying Street View Perspectives on Urban Perception: The Case of Celoria Street in Milan
Urban environments significantly influence people’s perception and walkability. Advances in computer vision and the availability of open-source Street View Imagery (SVI) have increased the use of Google Street View (GSV) for perceptual predictions and walkability assessments. However, a critical issue arises from the discrepancies between GSV images, captured from street centerlines, and SVI taken from pedestrian perspectives on sidewalks. This study examines whether people’s perceptions and street element proportions derived from GSV images align with those from sidewalk viewpoints, providing a more accurate basis for urban studies. Taking Celoria Street in Milan as a case study, two sets of 360° panoramic images were collected, one from the street center and the other from the sidewalks. These images were processed using a pre-trained perception prediction model and image segmentation techniques to generate perception responses. Dynamic Time Warping (DTW) was applied to assess the consistency between the two datasets, while Ordinary Least Squares (OLS) regression was used to analyze the impact of viewpoint changes along the street scene. Findings indicate that differences in sampling perspectives can affect urban environment assessment and perception predictions. This study highlights the potential biases of GSV data for analyzing urban environments and perceptions, advocating for more cautious use of SVI to ensure robust predictions on urban perception and walkability.