  Detecting and mapping informal settlements encompasses several of the United
Nations sustainable development goals. This is because informal settlements are
home to the most socially and economically vulnerable people on the planet.
Thus, understanding where these settlements are is of paramount importance to
both government and non-government organizations (NGOs), such as the United
Nations Children's Fund (UNICEF), who can use this information to deliver
effective social and economic aid. We propose a method that detects and maps
the locations of informal settlements using only freely available, Sentinel-2
low-resolution satellite spectral data and socio-economic data. This is in
contrast to previous studies that only use costly very-high resolution (VHR)
satellite and aerial imagery. We show how we can detect informal settlements by
combining both domain knowledge and machine learning techniques, to build a
classifier that looks for known roofing materials used in informal settlements.
Please find additional material at
https://frontierdevelopmentlab.github.io/informal-settlements/.
