Machine Learning and Artificial Intelligence for Smart Visualization, Presentation, and Study of Architecture and Engineering in the Urban Environment: Visualizing City Progress

Machine Learning and Artificial Intelligence for Smart Visualization, Presentation, and Study of Architecture and Engineering in the Urban Environment: Visualizing City Progress

Andrea Giordano, Kristin Love Huffman, Rachele Angela Bernardello, Maurizio Perticarini, Alessandro Basso
DOI: 10.4018/978-1-6684-4854-0.ch009
OnDemand:
(Individual Chapters)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

This research experiments the theme of cultural heritage (CH) in architectural/engineering fields, located in urban space. Primary sources and new tactics for digital reconstruction allow interactive contextualization-access to often inaccessible data creating pedagogical apps for spreading. Digital efforts are central, in recent years based on new technological opportunities that emerged from big data, Semantic Web technologies, and exponential growth of data accessible through digital libraries – EUROPEANA. Also, the use of data-based BIM allowed the gaining of high-level semantic concepts. Then, interdisciplinary collaborations between ICT and humanities disciplines are crucial for the advance of workflows that allow research on CH to exploit machine learning approaches. This chapter traces the visualizing cities progress, involving Duke and Padua University. This initiative embraces the analysis of urban systems to reveal with diverse methods how documentation/understanding of cultural sites complexities is part of a multimedia process that includes digital visualization of CH.
Chapter Preview
Top

Methodology

Visualizing Cities testifies how new technologies have the ability to “revolutionize” research and teaching by implementing collaborative theory and practice in the field of Digital Humanities, to interpret, represent, teach and promote the knowledge of a city as space in time. In this sense, therefore, this research embraces one of the most innovative aspects of digital technologies: the awareness that the speculative thought of scholars / researchers does not express itself without a correct perception and treatment of 3D space (Giordano, 2019). Consequently, using various examples, the methodology of Visualizing Cities is articulated in four distinct phases for this type of investigation:

Key Terms in this Chapter

Transformation: The mutation of architectural/engineering structures during time, as an organism.

Building Information Model (BIM): Digital place for the adoption of open semantic interoperability and standards.

Urban Built Heritage (UBH): Buildings in historical cities, where is still mandatory a deep exploration of their characters and mutual networks.

Neural Radiance Fields (Nerf): Systems structured to reconstruct the multilevel 3D spatial perception in order to define the density of the objects and their colorimetric properties.

Interoperability: Not only interconnection between computers, but now operative interrelation in digital dataset.

Reconfiguration: Interpretative system of built reality, not only from a formal point of view.

Augmented Digital Twin (ADT): Innovative data management, moving beyond the physical surface and defining a multidimensional ontological 3D structure.

Complete Chapter List

Search this Book:
Reset