The emergence of urban morphology as a distinct strand within architectural literature, focusing on core components like streets, buildings, and blocks, sets the context for this study. Among these elements, urban blocks hold a pivotal role due to their central position and interactions with other components. This research concentrates on urban blocks, specifically focusing on their detection and classification using machine learning techniques. It delves into the interplay between urban morphology and advancements in artificial intelligence (AI), aiming to integrate these fields to gain deeper insights into urban form elements.

In addition to the introductory chapter, this study is organized into three distinct parts, each with a specific focus and set of objectives. The first part is devoted to developing a theoretical framework on the mapping of urban morphology in relation to AI applications, emphasizing the contemporary shift towards quantitative and data-driven approaches in analysing urban form. This part delves into the quantification of urban form, the role of data-driven studies in urban analysis, and the critical impact of AI and remote sensing technologies in this field. It also presents a comprehensive review of various definitions found in the literature and introduces a novel, systematic approach for defining this concept. This part includes chapters 1 and 2.

The second part, representing the core of the thesis, centres on the model application, offering a detailed workflow, analytical framework, and insights into the extraction process, delving into the detailed application of a model incorporating deep learning on exemplifying urban block detection and classification. This part includes chapter 3.

The final part focuses on the practical application of the method developed in this study. It delves into the urban block classification, analysing the results derived from applying the model in different cities. This section examines the use of predefined metrics, conducts comparative analyses of clusters both within and across cities, and extends into the taxonomic comparison of two approaches, the conventional method, where blocks are defined based on their constituent elements (streets, plots, buildings) and classified based on their shape and size and the AI-driven approach. Additionally, it includes a thorough discussion on the feasibility and implications of this approach, thereby offering valuable insights into the future intersection of urban morphology and machine learning. This part includes chapters 4 and 5.

This research leverages high-resolution satellite imagery, capturing an extensive and diverse spectrum of urban forms from cities across Europe, America, and Asia. These images are accurately labelled to create a training dataset, an indispensable element in machine learning applications. In fact, the premise of supervised machine learning lies in training a model on a subset of data for which the variables of interest, those to be predicted, are known. In this study, the training dataset comprises a large collection of urban form images, with the urban block, the primary variable of interest, explicitly identified and marked by the researcher. Once the model ‘learns’ from this set of data, it can be used to make predictions on the presence or absence of urban blocks in previously unseen data, where the variable of interest is initially unknown.

The outcomes of this study delineate a comprehensive path for urban researchers to detect and classify urban forms. The results include the development of a taxonomy and a detailed analysis of its indicators, grounded in relevant literature. Beyond its conceptual contributions, the preliminary findings offer a glimpse into the outcomes of training and the evaluation of the supervised machine learning model utilized for the prediction and classification of urban blocks. This research marks a significant advancement in the integration of AI and machine learning techniques with urban morphology practices, laying the groundwork for a novel trajectory in future studies at this intersection. The study not only contributes to theoretical frameworks but also provides practical insights, exemplifying the potential of advanced technologies in reshaping urban research methodologies.

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Arch•i – Architectural Intelligence

The city is now producing a brand-new quality and kind of information on itself, in the form of data that can be stored, organised and analysed. Current technical needs are format standardisation, information gathering, management and selection, data processing and visualisation. This is leading to unprecedented developments of tools. New networks of relationships between documents can be defined and rapidly redefined, according to continuously updating needs and contents. In this complex information topology, the physical form of architecture maintains a key role, on which even the most up-to-date processing can be founded.

Italy has 49 cultural sites on the UNESCO World Heritage Site list. Despite this, only 0.7% of Italian GDP is allocated to culture¹. Sites such as the Imperial Fora in Rome or Pompeii and Herculaneum host millions of visitors each year, but cannot provide appropriate information services on site and face management and maintenance problems. At the same time, due to low tourist flows, many small, isolated or less well-known historical and archaeological sites cannot afford surveillance and maintenance and are thus not accessible. The available resources need to be optimised in order to ensure the protection of cultural heritage and to enhance its value.

The research project entitled Arch•i – Architectural Intelligence concerns the development of digital technologies to enhance the accessibility and management of cultural sites and the exploration of the related digital information. It is thought that mobile computing technologies can overcome the limitations of traditional information tools and allow novel interactions with monuments and works of art. The project is currently employing artificial intelligence (AI) to make data networks and digital environments accessible from the physical space.

Deep learning per l’architettura

In recent years, the diffusion of large image datasets and unprecedented computational power has boosted the development of a class of AI algorithms referred to as deep learning (DL). Among DL methods, convolutional neural networks (CNNs) have proven particularly effective in computer vision, finding applications in many disciplines. While AI is just beginning to interact with the built environment through mobile devices, heritage technologies have long been producing and exploring digital models and spatial archives. Hence, the digitalisation of cultural information offers structured and ready-to-use sources of knowledge that can be retrieved through the flexible features of AI. The interaction between DL and state-of-the-art information modelling is an opportunity to both exploit heritage databases and optimise new object recognition techniques. A specific approach to automated architecture recognition could change the way in which data on the urban environment is collected, processed and analysed, and could provide more effective ways to access data.

The Arch•i project has developed a mobile app that allows users to access data about buildings and works of art simply by pointing the camera at the object. The app is capable of connecting the real city to relevant, context-aware documents such as images, texts, maps and 3D models. It builds on original developments of CNN techniques aimed at recognising architectural features. The app is based on two main blocks of software: (1) an online, geographic-enabled database that makes it possible to upload different types of document and the related information or metadata; (2) the DL part, which is stored on the device and requires a very small amount of disk space.

The app offers an access point to a yet under-exploited network of digital information, not through a catalogue or a predefined route on a map, but just by framing the urban context through a mobile camera. A vast amount of multimedia information can be linked to the elements of a city, answering questions on how to make the newly available information easily and sustainably reachable.

I passi successivi

The Central Archaeological Area in Rome and the historical centre of Turin are the first test fields of the ‘AI guide’ developed. CNNs are, however, general models and can be trained to recognise a wide range of objects in different contexts. It is therefore planned to extend the project to other sites, covering different scales and time spans.

Further developments are also planned for the integration of the proposed AI technologies and semantic spatial databases, in order to: (1) make the system more scalable, to store online large amounts of data that can be retrieved when needed; (2) exploit the interoperability of the spatial information, i.e. connecting building information modelling (BIM) data to the environment explored through the app; (3) allow access to detailed information, i.e. performing DL recognition on the scale of building details, thus recognising monument parts or categories of constructive elements, decorations and materials.

Furthermore, we are bridging our first experiments with AI and other technologies:

• AR allows the interaction with 3D digital models and can superimpose precise spatial information layers on live images of the real environment.
• 5G cellular mobile communications will make large amounts of data immediately available, redefining location-based services and content access.
• IoT devices can enable access control and enhance on-site experience, providing cost-effective monitoring solutions which do not need the physical presence of supervising personnel.

The work carried out also points out possible connections between the virtual environment and the contemporary city. DL models could be trained to recognise building types or structural components, while the related information could integrate energy performance, structural behaviours and construction phases.

The underlying assumption of the research is that architecture has a key role in approaching technologically advanced tools. Form is a means to identify physical, observable and tangible facts and it can be used to produce shared models of the complex and multi-layered urban space. On this basis, our project intends to contribute to recognition, structuring and operational use of architectural form.

Notes

Fonte: Eurostat, Spesa totale delle amministrazioni pubbliche per “ricreazione, cultura e religione”, 2015. https://ec.europa.eu/eurostat/web/products-eurostat-news/-/DDN-20170807-1 (consultato il 26 marzo 2019)

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