  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 forest 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 on 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 work
ow.
