In regions where data about people’s welfare is expensive to collect, or not publicly available, international organizations are increasingly turning to satellite images to fill in gaps.
Satellites are best known for helping smartphones map driving routes or televisions deliver programs. But now, data from some of the thousands of satellites orbiting Earth are helping track things like crop conditions on rural farms, illegal deforestation, and increasingly, poverty in the hard-to-reach places around the globe.
As much as that data has the potential to provide invaluable information to humanitarian organizations, watchdog groups, and policymakers, there is too much of it to sift through in order to draw insights that could influence important decisions. A team of researchers from Stanford University, however, says it has developed an efficient way. By creating a deep-learning algorithm that can recognize signs of poverty in satellite images – such as condition of roads – the team sorted through a million images to accurately identify economic conditions in five African countries, reported the scientists in the journal Science on Thursday.
“For the majority of the world, we don’t have any labels for [satellite] images, so it’s not like people have gone and looked at satellite imagery and said, ‘Ok, here’s a house, here’s a tree, here’s a road,’” Neal Jean, a graduate student in electrical engineering at Stanford University and lead author on the Science paper, tells The Christian Science Monitor. “Since there’s so much imagery, a big part of the problem that we face…is figuring out how to extract useful information from this unstructured data.”
Nailing down how to do this would be a boon for international efforts to track poverty and take stock of general economic conditions around the globe. In some parts of the developing world, international aid organizations such as the World Bank are experimenting with using satellite surveys to collect data remotely, instead of in person, house by house — a tactic that could save both time and money. In places where there is unreliable data or none available at all, such as in North Korea, satellite photos showing no lights in the country versus the illumination of the world around it, can provide the only insights into economic activity on the ground.
“Satellite photos provide a level of geographic specificity that national accounts do not,” wrote Sendhil Mullainathan, a Harvard University economics professor, in The New York Times this spring.
Accurate information about people’s needs could influence decisions about where to send aid or build roads or hospitals. On a larger scale, such geographic specificity could help track whether global efforts to reduce poverty in some regions are paying off.
As Mr. Jean and his team point out in Science, “data gaps on the African continent are particularly constraining.” In the first decade of this century, 39 out of 59 African countries conducted fewer than two national surveys that could help paint a picture of poverty conditions there, according to the World Bank. Most of that data is not even publicly available. And 14 countries had no surveys at all.
“These shortcomings have prompted calls for a ‘data revolution’ to sharply scale up data collections efforts within Africa and elsewhere,” writes Jean and his co-authors.
In response, many efforts are underway to apply advanced technologies to poverty alleviation efforts.