Generating Material Maps to Map Informal Settlements

TL;DR

This paper presents a cost-effective method for detecting and mapping informal settlements using freely available Sentinel-2 satellite data combined with socio-economic information, aiding global development efforts.

Contribution

It introduces a novel approach that leverages low-resolution satellite imagery and domain knowledge to identify informal settlements, reducing reliance on expensive high-resolution data.

Findings

Successfully detects informal settlements using Sentinel-2 data

Combines domain knowledge with machine learning for classification

Provides a scalable method for global mapping of informal settlements

Abstract

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/.