# Case study: Mapping potential informal settlements areas in Tegucigalpa with machine learning to plan ground survey

arXiv: https://arxiv.org/abs/2006.14490 · HTML: https://mlsanity.com/paper/2006.14490/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

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

- [Mapping Slums with Medium Resolution Satellite Imagery: a Comparative Analysis of Multi-Spectral Data and Grey-level Co-occurrence Matrix Techniques](https://mlsanity.com/paper/2106.11395/) (2021)

## Abstract (arXiv)

Data collection through censuses is conducted every 10 years on average in Latin America, making it difficult to monitor the growth and support needed by communities living in these settlements. Conducting a field survey requires logistical resources to be able to do it exhaustively. The increasing availability of open data, high-resolution satellite images, and free software to process them allow us to be able to do so in a scalable way based on the analysis of these sources of information. This case study shows the collaboration between Dymaxion Labs and the NGO Techo to employ machine learning techniques to create the first informal settlements census of Tegucigalpa, Honduras.