Generating Material Maps to Map Informal Settlements
This work addresses the challenge of locating informal settlements for government and NGOs to deliver aid, but it is incremental as it builds on existing detection methods by using lower-cost data.
The paper tackles the problem of detecting and mapping informal settlements by proposing a method that uses freely available Sentinel-2 low-resolution satellite data and socio-economic data, achieving detection through a classifier that identifies known roofing materials.
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/.