A Topic Modeling Approach to Classifying Open Street Map Health Clinics and Schools in Sub-Saharan Africa

no code implementations • 22 Dec 2022 • Joshua W. Anderson, Luis Iñaki Alberro Encina, Tina George Karippacheril, Jonathan Hersh, Cadence Stringer

Data deprivation, or the lack of easily available and actionable information on the well-being of individuals, is a significant challenge for the developing world and an impediment to the design and operationalization of policies intended to alleviate poverty.

Monitoring War Destruction from Space: A Machine Learning Approach

no code implementations • 12 Oct 2020 • Hannes Mueller, Andre Groger, Jonathan Hersh, Andrea Matranga, Joan Serrat

Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete and potentially biased.

BIG-bench Machine Learning Data Augmentation +1

Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico

no code implementations • 16 Nov 2017 • Boris Babenko, Jonathan Hersh, David Newhouse, Anusha Ramakrishnan, Tom Swartz

We find that 1) the best models, which incorporate satellite-estimated land use as a predictor, explain approximately 57% of the variation in poverty in a validation sample of 10 percent of MCS-ENIGH municipalities; 2) Across all MCS-ENIGH municipalities explanatory power reduces to 44% in a CNN prediction and landcover model; 3) Predicted poverty from the CNN predictions alone explains 47% of the variation in poverty in the validation sample, and 37% over all MCS-ENIGH municipalities; 4) In urban areas we see slight improvements from using Digital Globe versus Planet imagery, which explain 61% and 54% of poverty variation respectively.