# Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data

## TL;DR

This paper introduces a new machine learning dataset and demonstrates that informal settlements can be detected using low-resolution satellite data, offering cost-effective mapping solutions for NGOs.

## Contribution

It provides a new dataset for informal settlement detection and shows that low-resolution data can be effectively used, unlike previous reliance on expensive high-resolution imagery.

## Key findings

- Low-resolution data can reliably detect informal settlements.
- Two classification schemes are effective for mapping settlements.
- A semi-automated pipeline converts satellite images into settlement maps.

## Abstract

Informal settlements are home to the most socially and economically vulnerable people on the planet. In order to deliver effective economic and social aid, non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), require detailed maps of the locations of informal settlements. However, data regarding informal and formal settlements is primarily unavailable and if available is often incomplete. This is due, in part, to the cost and complexity of gathering data on a large scale. To address these challenges, we, in this work, provide three contributions. 1) A brand new machine learning data-set, purposely developed for informal settlement detection. 2) We show that it is possible to detect informal settlements using freely available low-resolution (LR) data, in contrast to previous studies that use very-high resolution (VHR) satellite and aerial imagery, something that is cost-prohibitive for NGOs. 3) We demonstrate two effective classification schemes on our curated data set, one that is cost-efficient for NGOs and another that is cost-prohibitive for NGOs, but has additional utility. We integrate these schemes into a semi-automated pipeline that converts either a LR or VHR satellite image into a binary map that encodes the locations of informal settlements.