19th WORLD CONFERENCE OF THE ASSOCIATED RESEARCH CENTRES FOR THE URBAN UNDERGROUND SPACE, Belgrade, Serbia, November 4-7, 2025. (Paper No: 5.1.136, pp. 794-795)
АУТОР(И) / AUTHOR(S): Michael Doyle
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DOI: 10.46793/ACUUS2025.5.1.136
САЖЕТАК / ABSTRACT:
City streets tend to be conceptualized from a viewpoint of a particular or general observer. Smaller-scale studies and interventions focus on the perspectival and photographic views of particular streets, providing rich but highly nuanced information. On a larger scale, streets are elements within orthographic and axonometric views from a general observer placed at infinity, situating them in a standardized way within a single coordinate space. These representations require a selection of what will be shown. As research into the urban subsurface has observed, disciplines whose work is directly impacted by the geological conditions of a site are rarely confronted by geology in the early phases of the design process. The urban underground tends to remain hidden. Long-term holistic and multidimensional planning, which includes the subsurface as one among many characterizations of the location of a project, is complicated by the increasing number of dimensions that compete for priority in the politics of territorial transformation. Geographical information systems (GIS) have helped centralize and standardize heterogeneous data sources. These information management platforms have been accompanied by work conducted on how to synthesize that data and present it on a large scale. What has yet to be explored extensively is how geology can be looked at through a high-dimensional data model that harnesses the pattern-seeking capabilities of machine-learning techniques. From the standpoint of our contemporary information technology, artificial intelligence should be able to provide an impersonal viewpoint from which to look at all the available data for a street on a planetary scale.
КЉУЧНЕ РЕЧИ / KEYWORDS:
artificial intelligence, geospatial data, geology, urban streets, computational methodology
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