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Scientists combine satellite data and machine learning to map poverty
From phys.org:
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One of the biggest challenges in providing relief to people living in poverty is locating them. The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world, particularly on the African continent. Aid groups and other international organizations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct.
In the current issue of Science, Stanford researchers propose an accurate way to identify poverty in areas previously void of valuable survey information. The researchers used machine learning - the science of designing computer algorithms that learn from data - to extract information about poverty from high-resolution satellite imagery. In this case, the researchers built on earlier machine learning methods to find impoverished areas across five African countries.
"We have a limited number of surveys conducted in scattered villages across the African continent, but otherwise we have very little local-level information on poverty," said study coauthor Marshall Burke, an assistant professor of Earth system science at Stanford and a fellow at the Center on Food Security and the Environment. "At the same time, we collect all sorts of other data in these areas - like satellite imagery - constantly."
The researchers sought to understand whether high-resolution satellite imagery - an unconventional but readily available data source - could inform estimates of where impoverished people live. The difficulty was that while standard machine learning approaches work best when they can access vast amounts of data, in this case there was little data on poverty to start with.
more ...
[/center]
One of the biggest challenges in providing relief to people living in poverty is locating them. The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world, particularly on the African continent. Aid groups and other international organizations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct.
In the current issue of Science, Stanford researchers propose an accurate way to identify poverty in areas previously void of valuable survey information. The researchers used machine learning - the science of designing computer algorithms that learn from data - to extract information about poverty from high-resolution satellite imagery. In this case, the researchers built on earlier machine learning methods to find impoverished areas across five African countries.
"We have a limited number of surveys conducted in scattered villages across the African continent, but otherwise we have very little local-level information on poverty," said study coauthor Marshall Burke, an assistant professor of Earth system science at Stanford and a fellow at the Center on Food Security and the Environment. "At the same time, we collect all sorts of other data in these areas - like satellite imagery - constantly."
The researchers sought to understand whether high-resolution satellite imagery - an unconventional but readily available data source - could inform estimates of where impoverished people live. The difficulty was that while standard machine learning approaches work best when they can access vast amounts of data, in this case there was little data on poverty to start with.
more ...
