What can generative AI reveal about the relationship between urban poverty and water-related indicators?

This briefing presents the findings of a study that examined the potential of using non-traditional remotely sensed water indicators to calibrate and train an AI model for the automated detection of poverty hotspots. It presents a spatial relationship between wealth and water-related indicators such as flood exposure, infrastructure and sanitation. These findings and methods provide an evidence base for future interventions that look to combat extreme poverty in Tanzania.

Full study: https://doi.org/10.55158/DEEPCFWP2