In the urban sphere, AI is often associated with the concept of smart cities and thus the use of technology and the enormous computing power of machines to increase the quality of life for citizens, creating greater efficiency in resources and services, but there are further applications. The sprawl of information technologies is changing relationships between people and space, contributing to the mutation of iconographic production and the way content is conceived and communicated. Digital management and communication processes focus on images. The set of images constitutes a highly evocative language; it can be immediately comprehensible or require decoding that refers to specific contexts and cultures. Their importance is evident in human-targeted communication, but they also constitute a data transmission vehicle for empirical knowledge generation by algorithms like those used for artificial image creation from other images. Text-to-image AI generators are trained through the analysis of hundreds of millions of images and their related textual descriptions, which allows the system to learn the relationship between text and visual elements. Through this process, the network is also able to infer other information about reality. Images can be the representation of actual objects, as well as a subjective product of imagination or sensory processing. So too are the images created by Italo Calvino’s Invisible Cities, which bear witness to mental and non-geographical spaces. Cities that cannot be seen can be constructed from their poetical descriptions. What textual variables affect the realization of an image? How do the datasets that different text-to-image tools draw on affect image realization? Can these tools be trained from the user’s realization of an image? The paper collects the first outcomes of a research project comparing the results produced by the main image-generation tools from text descriptions extracted from Calvino’s book.
Is a Picture Worth a Thousand Words? Comparative Evaluation of Generative AI for Drawing and Representation
The paper presents a comparative evaluation of generative AI systems for drawing and architectural representation, focusing on the operational principles, workflows, and visual outputs of text-to-image applications such as Midjourney, Stable Diffusion, and DALL-E 2. The research analyzes neural networks, latent space mechanisms, generative adversarial networks, autoregressive models, and diffusion probabilistic models to explain how AI systems transform textual prompts into visual representations. Through experimental prompt engineering and comparative image generation tests, the study investigates the relationship between AI-assisted creativity, visual storytelling, representation processes, and design workflows. The paper critically discusses the implications of generative AI for architecture and visual culture, including authorship, ethics, copyright, bias, realism, and the transformation of creative practices. The research ultimately proposes an informed and critical approach to AI-assisted representation, emphasizing the evolving role of designers as curators and strategic decision-makers within AI-driven creative environments.
Artificial Intelligence and Virtual Reality in the Simulation of Human Behavior During Evacuations
This paper investigates the integration of artificial intelligence and virtual reality for the simulation of human behavior in emergency evacuation scenarios. The research focuses on the use of game engines and agent-based modeling to reproduce dynamic interactions between individuals and built environments under hazardous conditions.
The methodology combines three-dimensional architectural modeling, real-time simulation environments, and AI-driven behavioral models. As described in the workflow (pp. 755–756), architectural spaces are modeled and imported into Unreal Engine, where virtual agents are endowed with physical and behavioral properties. Agent movement is governed by social force models, allowing the simulation of individual decision-making processes influenced by environmental stimuli, obstacles, and crowd dynamics. The system supports real-time interaction and visualization, enabling the testing of evacuation scenarios under varying conditions such as fire location and spatial configuration. Results demonstrate that immersive simulation environments can support predictive analysis, training, and design evaluation, while also highlighting the limitations of current models in capturing complex human behavior and decision-making processes during emergencies.
Proposal for a Data Visualization and Assessment System to Rebalance Landscape Quality
The landscape can be considered a complex system described as a non-linear entity, organized accord-ing to the connections between the different elements that characterize its state. The latter cannot be determined a priori but emerges from the multiple interactions between assets and relationships that are no longer found when the phenomenon is traced back to the individual components. Territorial development is closely connected to social, economic, cultural and symbolic issues that determine the transformative practices of space and the territorial palimpsest. If not carefully managed, these forces can lead to the dissolution of the landscape and environmental values that have been stratified in a specific land. This paper proposes the construction of a numerical spatial model that describes the territorial settlement patterns, based on methodologies and techniques typical of AI, to develop tools for re-balancing the man-landscape relationship.
Artificial Intelligency, Big Data and Cultural Heritage
In recent decades, the cultural heritage sector has benefited from solutions offered by ICT for the conservation, management, enhancement and communication of cultural heritage; today this specific sector benefits from the infinite potential of application of AI. The proposed research identifies the three main lines of research that operate in the cultural heritage exploiting the synergy between machine learning, big data and AI, starting from the analysis of the state of the art and a subsequent first taxonomic approximation of artificial intelligence systems. The analysis of some case studies developed in the field of recovery and restoration of cultural heritage, monitoring and prevention of damage, data acquisition and analysis of the same, confirm the real potential of AI: trigger knowledge from knowledge.