The paper is focused on an ongoing project funded by the Emilia-Romagna region and aimed at the creation of a new workflow finalizing digital data from integrated survey towards an “adaptive” Building Information Modeling (BIM). The project AIM-eBIM—Adapted Information Management for existing Buildings Information Modeling—brings together regional research laboratories and companies to pursue industrial research topics towards a greater deployment of digital tools. Digital surveying has triggered huge potential for innovation, but also generated new challenges in managing and using large amounts of data, often left unused. The quantity of surveyed data used to document built or Cultural Heritage often does not correspond to the quality or reliability of information. Moreover, parametric modeling of existing heritage through BIM is becoming as pervasive as it is necessary, considering regulatory trends. However, these tools can be ineffective from the point of view of users (professionals, companies) who must deal with such complexity. The challenge is to bring discretization (and simplification) processes to source data toward easier informative integration into BIM models, by facilitating and enhancing interpretation needs. In this direction, Artificial Intelligence (AI) algorithms are part of the process. The adapted informative implementation of parametric models is based on digital source data (laser and photogrammetry) segmentation by AI according to specific topics (documentation, analysis, monitoring, conservation, project) and criteria (materials, techniques, components, structures).
Reliability of Human-genAI Integrated Process for the Virtual Reconstruction of a Lost Architecture
The contribution examines the case study of the Church of S. Pietro a Coppito in L’Aquila, which underwent significant stylistic restoration in the 1960s, to explore the potential applications and reliability of generative AI in cultural heritage communication. In particular, according to the virtual reconstruction of the ancient configuration, the study investigates the impact of integrating graphical inputs alongside textual prompts for image generation, assessing their effectiveness as a means of guiding AI and improving the quality of the results achieved. Based on graphic, photographic, and historiographical sources, sketches of the interior views of the church were produced. Subsequently, various gen-AI platforms were tested to process these images, evaluating the algorithms’ ability not only to generate coherent and effective visuals but, above all, to understand and interpret the initial graphic inputs. The results show both potential and critical limitations: while AI can produce photorealistic and stylistically coherent renderings, it often introduces arbitrary elements and struggles with strict historical accuracy, especially in architectural and decorative details. The paper highlights the need for deeper human-AI interaction and emphasizes the importance of informed graphical input and critical evaluation in the application of genAI to cultural heritage representation.
Artistry, Technology and Challenges: The Subtle Balance Between Fake Results and Real Integration in the Use of AI for Image Generation on Medieval Frescos Reconstructions
AI-based image-generative tools significantly enhance creativity and digital reconstruction by accelerating workflows for reconstructing digital heritage. These tools apply to various contexts, such as archaeological sites, transformed urban areas, damaged buildings, unbuilt architecture, and partially lost artworks like mural paintings and frescoes. In reconstructing such works, the process is highly complex and requires scholars with expertise in recognizing and attributing fragments and better-preserved pieces. While it remains necessary to suggest potential lines, figures, or scenes, the outcome often involves multiple possibilities. AI image generators can support these challenging tasks by integrating them into workflows, from producing stochastic results to extending patterns and coloured areas. However, their use, especially via “off-the-shelf” software, introduces two key challenges. The first concerns achieving accurate reconstructions that maximize the AI’s potential while avoiding casual or tentative outputs. This involves addressing limitations in existing AI systems and ensuring the final results are both reliable and time efficient. The second challenge relates to cultural evaluation. The line between AI-assisted reconstruction and creating entirely fake results is delicate. Misuse could exploit public fascination with AI or lead to errors and misinterpretations without expert oversight. This integration is in its early stages, necessitating rigorous testing and exploration. The proposed contribution analyzes the digital reconstruction of medieval frescoes from central Italy, blending traditional methods with significant AI inputs. These case studies highlight the balance between human-driven processes and the acceptance of digital outputs, offering reflections on the evolving relationship between human expertise and AI-driven creativity.
The Language of Photography in the Age of AI
Text-to-image algorithms based on Deep Learning are central to content creation in multiple application domains. In the last few years, the capacity of Neural Networks to generate increasingly realistic images quickly has blurred the boundary between authentic and realistic content, making genuine and false data less and less distinguishable. This condition leads to a profound reflection on the application of photographic images as a tool for communication and storytelling, trying to answer simple questions. Can today’s Neural Networks generate content comparable and indistinguishable from a photograph in both formal and compositional terms? Can artificial intelligence algorithms replace the photographer’s ability to design and obtain images that preserve the story and the place’s intangible culture? From a set of photographic rules framed in specific workflows, the research analyses some results obtained using text-to-image algorithms within the Midjourney program. The experiment aims to determine the pros and cons of using text-to-image algorithms to automatically generate photographic images, highlighting the potential and current limitations in constructing content subject to specific formal rules.
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.
Artificial Intelligence Alternatives for the Digitisation of Cultural Heritage, 3D Rendering Processes of the Sala Basile in Villa Igiea
The technology integrated with Artificial Intelligence functionality allows the acquisition of real spaces in an increasingly expeditious manner, giving the possibility of digitally configuring complex environments and experimenting with new digital surveying methodologies that differ from the established image/range-based techniques. In recent years, substantial advancements in the fields of computer graphics and computer vision have led to the emergence of innovative approaches, such as Gaussian Splatting, which have revolutionized 3D scene reconstruction and rendering processes. These methods offer remarkable improvements in both realism and computational efficiency. Moreover, in the digital age, the demand for accessible and user-friendly applications is constantly growing, and research is increasingly focused on solutions that offer essential functionality at cost-effective prices. These objectives are mainly pursued by exploiting open-source frameworks, cloud services, and simple methodologies to keep development costs low. The paper aims to validate the results of two Gaussian Splatting processes generated by accessible and user-friendly applications, as well as using simple data as a starting point, such as videos recorded with 360-degree cameras. In this way, the work seeks to evaluate the effectiveness of these innovative techniques in producing high-quality 3D reconstructions, considering the simplicity of the process for users and the associated costs.
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.
An Educational Experience Between AI and Architectural Drawing
This paper presents the latest phase of a research project investigating the interplay between the representation of ‘Virtual Living’ and digital technocultures—particularly the integration of Artificial Intelligence (AI) and Extended Reality (XR)—within the educational framework of an Architectural Drawing course. This course is part of the third-year curriculum of the Bachelor’s Degree in Architecture at the ‘G. d’Annunzio’ University of Chieti-Pescara. The research builds upon and refines technocultural methodologies long employed in teaching, leveraging the concept of the ‘semantic model’ as a versatile foundation for designing habitable virtual spaces, such as metaverses or virtual museums. This approach has been revisited and expanded to address the rapid evolution of generative AI applications, which demand rigorous monitoring and continuous thematic experimentation. New technologies in representation are reshaping the pedagogical landscape, offering unprecedented opportunities to redefine the scope of architectural drawing education. From descriptive geometry to surveying, from the history of representation to design, the incorporation of AI has fundamentally transformed how visual representations are conceived and executed. This paper discusses a case study that bridges research and pedagogy, showcasing how students’ creativity, when coupled with the capabilities of AI, facilitates the creation of innovative semantic models. These models have direct applications in the design of Virtual Cities and Museums, offering a vision of inhabitable spaces within the metaverse.
Integration of AI-Based Methodologies for Surveying and Virtual Reconstruction: The Case of the Chiostro and the Cappella della Pace in the Monastery of Santi Giovanni e Paolo in Venice
The case study described below is part of a larger project entitled “RE-LIFE: accessibility and inclusiveness in the scenarios of reuse and enhancement of former monastic buildings” at the University of Padua, funded by the European Commission and still under development. The project involves the use of Building Information Modelling (BIM) reconstruction, historical-architectural analysis, accessibility studies, and the use of virtual and augmented reality to highlight, through a timeline, the various construction phases and works lost over the centuries. The present article focuses on the case of Santi Giovanni e Paolo and on the Chiostro and Cappella della Pace, both of which no longer survive. The objective of the article is to delineate the historical and graphic workflow pursued to reconstruct the chapel, a hypothesis based on the analysis of precise historical-graphic sources and on the digital survey. The latter was carried out on the cloister through photogrammetry using a Fujifilm MILC camera and an Insta360 X4 8K. The focus is on the use of 360 video shooting techniques for photogrammetric survey processing and on the possibility of integrating a Gaussian Splatting survey for the development of immersive realities, such as applied games or virtual tours. The article provides a comprehensive explanation of how diverse representation techniques can be converged for a singular divulgative purpose.
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.