Urban Insights through Machine Learning in Data-Scarce Environments

​Polic​y 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​
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Sum​​mary

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.


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