Mapping Informal Settlements in Developing Countries with Multi-resolution, Multi-spectral Data
This addresses the challenge of locating informal settlements for governments and NGOs like UNICEF to deliver aid, but it appears incremental as it builds on existing remote sensing techniques.
The paper tackles the problem of detecting and mapping informal settlements in developing countries by proposing two methods: one using low-resolution Sentinel-2 imagery with noisy annotations and another using costly very-high-resolution imagery with deep learning. It claims to be the first to successfully map informal settlements with low-resolution satellite imagery, with extensive evaluation and comparison provided.
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 https://frontierdevelopmentlab.github.io/informal-settlements/.