Scientists combine satellite data and machine learning to map poverty

Stanford scientists combine satellite data and machine learning to map poverty
Stanford researchers combine high-resolution satellite imagery with powerful machine learning algorithms to predict poverty in Nigeria, Uganda, Tanzania, Rwanda and Malawi. Credit: Neal Jean et al.

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 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.

"There are few places in the world where we can tell the computer with certainty whether the people living there are rich or poor," said study lead author Neal Jean, a doctoral student in computer science at Stanford's School of Engineering. "This makes it hard to extract useful information from the huge amount of daytime that's available."

Because areas that are brighter at night are usually more developed, the solution involved combining high-resolution daytime imagery with images of the Earth at night. The researchers used the "nightlight" data to identify features in the higher-resolution daytime imagery that are correlated with economic development.

"Without being told what to look for, our machine learning algorithm learned to pick out of the imagery many things that are easily recognizable to humans - things like roads, urban areas and farmland," says Jean. The researchers then used these features from the daytime imagery to predict village-level wealth, as measured in the available survey data.

They found that this method did a surprisingly good job predicting the distribution of poverty, outperforming existing approaches. These improved poverty maps could help aid organizations and policymakers distribute funds more efficiently and enact and evaluate policies more effectively.

"Our paper demonstrates the power of in this context," said study co-author Stefano Ermon, assistant professor of computer science and a fellow by courtesy at the Stanford Woods Institute of the Environment. "And since it's cheap and scalable - requiring only satellite images - it could be used to map poverty around the world in a very low-cost way."


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Researchers use dark of night and machine learning to shed light on global poverty

More information: "Combining satellite imagery and machine learning to predict poverty," Science, science.sciencemag.org/cgi/doi … 1126/science.aaf7894
Journal information: Science

Citation: Scientists combine satellite data and machine learning to map poverty (2016, August 18) retrieved 21 July 2019 from https://phys.org/news/2016-08-scientists-combine-satellite-machine-poverty.html
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Aug 18, 2016
they could just send robots over the red areas , then no one has to actually observe poor people.!!

Aug 19, 2016
Sigh, this isn't that complicated. You get poverty for several reasons. Eliminate those reasons and poverty will be greatly reduced. To greatly enhance an economy just do the following:

1) A non-corrupt government that uniformly enforces it's laws.
2) A government that respects property rights.
3) A government that minimizes it's meddling with the economy.
4) A culture that respects and rewards hard work.

With the above you will almost always have a booming economy and the wealth disparity would be greatly reduced. Will there be no poverty? Of course not, some people just aren't interested in working hard and sometimes bad luck intrudes. But overall the above is a tried and true means of 'spreading' the wealth among most of the population and leads to a much higher standard of living.

Tblakely, your list misses another important item, that should stand at number 1) Ensure that people don't have more children than they can offer a life in dignity and with perspectives. And never, ever, beyond 3 children per women.

Aug 22, 2016
To add to the list:
1. It's not only the having children as much as it's also children out of wedlock and the resultant single parent families or worse, orphans.
2. Divorce and separation with children in the mix.
3. Alcohol abuse, drugs and the resulting violence, insecurity and stress.
4. Disobedience to parents. Disrespect for and rebellion against elders.
5. Theft.
6. Murder.
7. Most of the above arising out of simple covetousness. Coveting what you do not have and believing that you cannot attain it by your own means so you have to have someone else's instead.
But above all, not believing in a God to whom you are accountable is the major cause of poverty, crime and violence. If you say you believe in this God but carry on regardless it's the same as not believing. A lot of those reading this website will disagree but they forget [or actually do not even realize] that their morals are based on the recognition of right and wrong from the Christian biblical worldview.

Aug 22, 2016
"But above all, not believing in a God to whom you are accountable is the major cause of poverty, crime and violence. If you say you believe in this God but carry on regardless it's the same as not believing."

Bigoted atheist bashing. It says more about your lack of ethics than anything else.

I have much more trust in the morality of someone who doesn't need an invisible sky daddy watching them 24/7 to keep them from bad behaviour.

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