Today's Hard|Forum Post
Today's Hard|Forum Post

Monday March 28, 2016

How GPUs Are Helping Map Worldwide Poverty

Eradicating worldwide poverty by 2030 is the top goal on the United Nations’ sustainable development agenda, published late last year. But a lack of data has frustrated efforts to measure progress toward the goal. Most of those living in extreme poverty are in sub-Saharan Africa and Southern Asia, where accurate poverty data is scarce. A small team at Stanford University is changing that, one satellite image at a time.

Machine learning expert Stefano Ermon partnered with food security specialists David Lobell and Marshall Burke, plus a couple Stanford engineering students, to turn Google Earth images into statistical poverty models. "We want to end extreme poverty, but we need a way to be able to measure whether we’re making progress or not," said Ermon, an assistant professor of computer science at Stanford.

Using NVIDIA GPUs, the team trained a neural network to accurately predict poverty levels in sub-Saharan Africa from image features like roads, farmlands and homes. This work has placed Stanford among five finalists for NVIDIA’s 2016 Global Impact Award. Each year, we award a $150,000 grant to researchers using NVIDIA technology for groundbreaking work that addresses social, humanitarian and environmental problems.

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