Mapping Informal Settlements in Developing Countries with Multi-resolution, Multi-spectral Data

Patrick Helber, Bradley Gram-Hansen, Indhu Varatharajan, Faiza Azam, Alejandro Coca-Castro, Veronika Kopackova, Piotr Bilinski

In: AI for Social Good Workshop. International Conference on Learning Representations (ICLR) May 6-9 New Orleans LA United States ICLR 2019.


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 two effective methods for detecting and mapping the locations of informal settlements. One uses only low-resolution (LR), freely available, Sentinel-2 multispectral satellite imagery with noisy annotations, whilst the other is a deep learning approach that uses only costly very-high-resolution (VHR) satellite imagery. To our knowledge, we are the first to map informal settlements successfully with low-resolution satellite imagery. We extensively evaluate and compare the proposed methods. Please find additional material at

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence