Machine learning for buildings' characterization and power-law recovery of urban metrics.
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ABSTRACT:
SUBMITTER: Krayem A
PROVIDER: S-EPMC7842992 | biostudies-literature | 2021
REPOSITORIES: biostudies-literature
Publications
Machine learning for buildings' characterization and power-law recovery of urban metrics.
Krayem Alaa A Yeretzian Aram A Faour Ghaleb G Najem Sara S
PloS one 20210128 1
In this paper we focus on a critical component of the city: its building stock, which holds much of its socio-economic activities. In our case, the lack of a comprehensive database about their features and its limitation to a surveyed subset lead us to adopt data-driven techniques to extend our knowledge to the near-city-scale. Neural networks and random forests are applied to identify the buildings' number of floors and construction periods' dependencies on a set of shape features: area, perime ...[more]
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