<div class="csl-bib-body">
<div class="csl-entry">Wurzer, G. P. X., Wolfgang E. Lorenz, & Bindreiter, S. (2026, June 26). <i>Investigating Urban Neighborhood Similarity : AI classification of urban neighbourhoods for material-stock recycling — a case study on machine learning vs. vector distance vs. expert knowledge in the city of Vienna</i> [Poster Presentation]. Interdisciplinary Workshop : AI in Science and Engineering, Vienna, Austria. https://doi.org/10.34726/12379</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/229348
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dc.identifier.uri
https://doi.org/10.34726/12379
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dc.description.abstract
Vienna holds an enormous, largely unmapped material stock — brick, wood and steel locked inside buildings that will eventually be demolished. To locate where similar recycling potential clusters, we partition the entire city into approximately 16,000 map tiles of roughly 200 m side length and task an artifi cial-intelligence workfl ow with assigning each tile one of fi ve characteristic Viennese building types. Experts (architects and urban planners) manually label only a small set of archetypal tiles; from these few examples, two automatic methods scale the classifi cation to the whole city: a hand-crafted vector-distance similarity and a supervised machine-learning classifi er based on decision trees and random forests. This contribution reports what the AI learned, how the two methods compare in accuracy, where the AI agrees with human experts, and — most revealingly — where and why it does not.
en
dc.description.sponsorship
FFG - Österr. Forschungsförderungs- gesellschaft mbH
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Cityscape Analysis
en
dc.subject
Artificial Intelligence
en
dc.subject
Machine Learning
en
dc.subject
Vector Distances
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dc.subject
Similarity Metric
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dc.subject
Material Stock
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dc.subject
Urban Mining
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dc.title
Investigating Urban Neighborhood Similarity : AI classification of urban neighbourhoods for material-stock recycling — a case study on machine learning vs. vector distance vs. expert knowledge in the city of Vienna
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.rights.license
Urheberrechtsschutz
de
dc.rights.license
In Copyright
en
dc.identifier.doi
10.34726/12379
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dc.relation.grantno
FO999886923_11112021_114909946
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dc.rights.holder
TU Wien, DAP
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dc.type.category
Poster Presentation
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tuw.publication.invited
invited
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tuw.project.title
Der Materialaspekt der Innenentwicklung (Materialressourcen der Stadt - Digitalisieren, Analysieren und nachhaltig Bewirtschaften 2)
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tuw.researchTopic.id
A2
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tuw.researchTopic.id
C6
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tuw.researchTopic.id
X1
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tuw.researchTopic.name
Urban and Regional Transformation
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tuw.researchTopic.name
Modeling and Simulation
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tuw.researchTopic.name
Beyond TUW-research focus
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tuw.researchTopic.value
20
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tuw.researchTopic.value
20
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tuw.researchTopic.value
60
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tuw.publication.orgunit
E259-01 - Forschungsbereich Digitale Architektur und Raumplanung
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tuw.publication.orgunit
E280-04 - Forschungsbereich Örtliche Raumplanung
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tuw.author.orcid
0000-0001-7846-4735
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tuw.author.orcid
0000-0002-8977-1885
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dc.rights.identifier
Urheberrechtsschutz
de
dc.rights.identifier
In Copyright
en
tuw.event.name
Interdisciplinary Workshop : AI in Science and Engineering
