# Mapping Slums with Medium Resolution Satellite Imagery: a Comparative Analysis of Multi-Spectral Data and Grey-level Co-occurrence Matrix Techniques

arXiv: https://arxiv.org/abs/2106.11395 · HTML: https://mlsanity.com/paper/2106.11395/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Canonical Correlation Forests](https://mlsanity.com/paper/1507.05444/) (2015)
- [Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data](https://mlsanity.com/paper/1901.00861/) (2019)
- [Case study: Mapping potential informal settlements areas in Tegucigalpa with machine learning to plan ground survey](https://mlsanity.com/paper/2006.14490/) (2020)

## Built on by (most cited first, 1 of 1)

- [Spatially-constrained clustering of geospatial features for heat vulnerability assessment of favelas in Rio de Janeiro](https://mlsanity.com/paper/2604.26133/) (2026)

## Abstract (arXiv)

The UN-Habitat estimates that over one billion people live in slums around the world. However, state-of-the-art techniques to detect the location of slum areas employ high-resolution satellite imagery, which is costly to obtain and process. As a result, researchers have started to look at utilising free and open-access medium resolution satellite imagery. Yet, there is no clear consensus on which data preparation and machine learning approaches are the most appropriate to use with such imagery data. In this paper, we evaluate two techniques (multi-spectral data and grey-level co-occurrence matrix feature extraction) on an open-access dataset consisting of labelled Sentinel-2 images with a spatial resolution of 10 meters. Both techniques were paired with a canonical correlation forests classifier. The results show that the grey-level co-occurrence matrix performed better than multi-spectral data for all four cities. It had an average accuracy for the slum class of 97% and a mean intersection over union of 94%, while multi-spectral data had 75% and 64% for the respective metrics. These results indicate that open-access satellite imagery with a resolution of at least 10 meters may be suitable for keeping track of development goals such as the detection of slums in cities.