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Generating Material Maps to Map Informal Settlements

Patrick Helber; Bradley Gram-Hansen; Indhu Varatharajan; Faiza Azam; Alejandro Coca-Castro; Veronika Kopackova; Piotr Bilinski
In: Maria De-Arteaga; Amanda Coston; William Herlands (Hrsg.). Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact. Machine Learning for the Developing World (ML4D-2018), located at NeurIPS2018, December 8, Montréal, Québec, Canada, arXiv, 2018.


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 this