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Machine learning for buildings’ characterization and power-law recovery of urban metrics

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Listed:
  • Alaa Krayem
  • Aram Yeretzian
  • Ghaleb Faour
  • Sara Najem

Abstract

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, perimeter, and height along with the annual electricity consumption, relying a surveyed data in the city of Beirut. The predicted results are then compared with established scaling laws of urban forms, which constitutes a further consistency check and validation of our workflow.

Suggested Citation

  • Alaa Krayem & Aram Yeretzian & Ghaleb Faour & Sara Najem, 2021. "Machine learning for buildings’ characterization and power-law recovery of urban metrics," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-13, January.
    • Handle: RePEc:plo:pone00:0246096
    DOI: 10.1371/journal.pone.0246096
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    References listed on IDEAS

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    1. M. Batty & R. Carvalho & A. Hudson-Smith & R. Milton & D. Smith & P. Steadman, 2008. "Scaling and allometry in the building geometries of Greater London," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 63(3), pages 303-314, June.
    2. Ravetz, Joe, 2008. "State of the stock--What do we know about existing buildings and their future prospects?," Energy Policy, Elsevier, vol. 36(12), pages 4462-4470, December.
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