deepLNAfrica

Problem:

This project aims at using geospatial machine learning to measure housing inequalities in urban areas in sub-Saharan Africa for use in development economics research. Better data could lead to better models to extract information to be used to design pro-poor policies. The goal is to set up a data pipeline to be used in accurate and robust machine learning methods, exploiting paired Earth observation data and local census data. As a central case study, deepLNAfrical aims at inventorying and mapping informal settlements in sub-Saharan Africa.

Proposed approach:

The SDSC leads the implementation and development of techniques to extract information from geolocated data, such as Earth observation satellite images, geolocated natural images, paired census data, and related attributes. We will focus on models which are adapted to extract information from such data and make them easily reusable by the community, therefore supporting research in the same direction.

Impact:

Measuring socioeconomic status at high spatial and temporal resolution is vital. In rapidly developing low and middle-income countries, population growth and urbanization rates are high, and data scarce. Emerging sources of large-scale data (e.g., satellite imagery, mobile phones, social media), have the potential to significantly improve the frequency and spatial resolution of the measurement of socioeconomic wellbeing. This could help to identify areas of concern earlier, which areas have the greatest needs, and investigate how those needs are linked to social and health outcomes.