Our method uses machine-vision (AI) techniques to process daylight and night-time satellite imagery. Using additional features and proprietary data, we are able to both now-cast and forecast poverty shares through 2030.
Working under the auspices of the Asian Development Bank and in collaboration with local National Statistical Offices, we used machine learning to develop highly granular poverty models and maps of Thailand and the Philippines. These models reveal the incidence of poverty and extreme poverty at the village level throughout each country.
Working in collaboration with the International Fund for Agricultural Development (IFAD), the Government of Niger and the Organisation International de la Fracophonie, we are piloting the development of a village level poverty model for Niger. Data from the project will be used to inform the further development of IFAD’s forthcoming 2020 Sahel strategy.