AI News, Machine-learning makes poverty mapping as easy as night and day

Machine-learning makes poverty mapping as easy as night and day

“The system essentially learned how to solve the problem by comparing those two sets of images.” Burke, Ermon and fellow team members David Lobell, an associate professor of Earth system science, undergraduate computer science researcher Michael Xie and electrical engineering PhD student Neal Jean detailed their approach in a paper for the proceedings of the 30th AAAI Conference on Artificial Intelligence.  Their basic technique – directing a model to compare images to predict a specific value – is a variant of machine learning known as transfer learning.

The system did this time and again, making day-to-night comparisons and predictions and constantly reconciling its machine-devised analytical constructs with details it gleaned from the data. “As the model learns, it picks up whatever it associates with increasing light in the nighttime images, compares that to daytime images of the same area, correlates its observations with data obtained from known field-surveyed areas and makes a judgment,” Lobell said.

“We can’t say with certainty what associations it is making, or precisely why or how it is making them.” Ultimately, the researchers believe, this model could supplant the expensive and time-consuming ground surveys currently used for poverty mapping. “This offers an unbelievable opportunity for cheap, scalable and surprisingly accurate measurement of poverty,” Burke said.

More imagery, acquired on a more consistent basis, would be needed to give their system the raw material to take the next step and predict whether locales are inching toward prosperity or getting further bogged down in misery.

I don’t think it will be too long before we’re able to do cheap, scalable, highly accurate mapping in time as well as space.” Even as they consider what they might be able to do with more abundant satellite imagery, the Stanford researchers are contemplating what they could do with different raw data – say, mobile phone activity.

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