Urban Insights through Machine Learning in Data-Scarce Environments

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Urban Insights through Machine Learning in Data-Scarce Environments

Policy Brief

Research Team

Alaa Krayem, Physics Department, American University of Beirut

Aram Yeretzian, Architecture Department, American University of Beirut

Ghaleb Faour, National Center for Remote Sensing, National Council for Scientific Research, CNRS-L

Ali Ahmad, Issam Fares Institute for Public Policy and International Affairs, American University of Beirut

Sara Najem, Physics Department, American University of Beirut

Energy Policy and Security Program, May 2020

Summary

Today, the existing building stock constitutes an important component of urban life as it holds much of a city’s socio-economic activities. Therefore, comprehensive building survey data is essential to support more effective policymaking related to the sustainable management of cities. However, such a database is not always available and is often limited to a surveyed subset of buildings. This policy brief reports a methodology developed to extend our knowledge to the entire present building stock by adopting data-driven techniques. As an application, the number of floors and the construction period of buildings were predicted at the near-city scale in the Beirut administrative area. The results can help to assess and plan resilience policies at the city level as well as assist demographic and risk management studies.

Read the full policy-brief:

"Urban Insights through Machine Learning in Data-Scarce Environments"

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