Component-Level
Residential Building Material
Stock Characterization Using
Computer Vision Techniques
Posted on 2024-02-09 - 16:03
Residential building material stock constitutes a significant
part
of the built environment, providing crucial shelter and habitat services.
The hypothesis concerning stock mass and composition has garnered
considerable attention over the past decade. While previous research
has mainly focused on the spatial analysis of building masses, it
often neglected the component-level stock analysis or where heavy
labor cost for onsite survey is required. This paper presents a novel
approach for efficient component-level residential building stock
accounting in the United Kingdom, utilizing drive-by street view images
and building footprint data. We assessed four major construction materials:
brick, stone, mortar, and glass. Compared to traditional approaches
that utilize surveyed material intensity data, the developed method
employs automatically extracted physical dimensions of building components
incorporating predicted material types to calculate material mass.
This not only improves efficiency but also enhances accuracy in managing
the heterogeneity of building structures. The results revealed error
rates of 5 and 22% for mortar and glass mass estimations and 8 and
7% for brick and stone mass estimations, with known wall types. These
findings represent significant advancements in building material stock
characterization and suggest that our approach has considerable potential
for further research and practical applications. Especially, our method
establishes a basis for evaluating the potential of component-level
material reuse, serving the objectives of a circular economy.
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Dai, Menglin; Jurczyk, Jakub; Arbabi, Hadi; Mao, Ruichang; Ward, Wil; Mayfield, Martin; et al. (2024). Component-Level
Residential Building Material
Stock Characterization Using
Computer Vision Techniques. ACS Publications. Collection. https://doi.org/10.1021/acs.est.3c09207
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AUTHORS (8)
MD
Menglin Dai
JJ
Jakub Jurczyk
HA
Hadi Arbabi
RM
Ruichang Mao
WW
Wil Ward
MM
Martin Mayfield
GL
Gang Liu
DT
Danielle Densley Tingley
KEYWORDS
street view imagesproviding crucial shelterlevel material reuseknown wall typesheavy labor costcalculate material massalso enhances accuracylevel stock analysisgarnered considerable attentionbuilding footprint datastone mass estimationsglass mass estimationsspatial analysisbuilding structuresbuilding massesutilizing driveunited kingdomtraditional approachessignificant partpractical applicationspast decadepaper presentsonsite surveyoften neglectedmethod establishesmainly focusedimproves efficiencyhabitat servicesconsiderable potentialcircular economybuilt environment