Case study: Mapping potential informal settlements areas in Tegucigalpa with machine learning to plan ground survey
It addresses the problem of infrequent data collection for informal settlements in Latin America, offering a scalable solution for NGOs and planners, though it is incremental in applying existing methods to a new context.
This study tackled the challenge of monitoring informal settlements in Tegucigalpa, Honduras, by using machine learning on satellite imagery and open data to create the first census of such areas, enabling more efficient ground survey planning.
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.