A machine learning approach of building forms classification for slum infrastructure upgrading. Take Cairo as an example

The self-build and conversion of slums has completely changed the original function and scale. The lack of building statistics in the slums presents a challenge for the renovation of old self-built construction facilities and the layout of new infrastructure.

The main aim is to provide a holistic methodology to study slum self- construction behaviour and develop a system to classifies and predicts the modelling of different building types only by satellite planes. Allowing the renewed infrastructure to serve more inhabitants and optimise access distances.

In order to do so, data collection, machine learning classification, and Building Performance Simulation, three main parts are used to build the system.As a result, the contribution is visualization guidance in physical space and policy for self-construction.

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