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. Orbital Insight, a Palo Alto satellite data analysis company, has worked with the World Bank to help the organization determine where it should allocate more than $100 billion worth of loans each year, as Bloomberg reports. The company uses machine learning to find clues of poverty in reams of photos. Its software counts cars, analyzes the height and shapes of buildings, and measures agricultural activity in remote villages.
“If you see more cars, or more cars over time, that could be an indicator of relative wealth in one village vs. another that hasn’t seen growth in cars over time,” Jeff Stein, vice president for business development at Orbital Insight told Bloomberg.
Joshua Blumenstock, a data scientist at University of California in Berkeley, uses proprietary mobile phone metadata in countries like Rwanda to analyze patterns of calls. Those patterns give clues to whether people in impoverished communities have jobs, among other indicators of wealth. And GiveDirectly, a New York City-based nonprofit that gives cash to poor people in countries including Kenya and Uganda, has experimented with mining satellite images to determine who should get donations. People living in houses with thatched roofs are more likely to be poor than those living in houses with metal roofs, the organization figures.
While these are all useful methods, Jean’s technique has some additional benefits. For one, his team used publicly available data. It included daytime satellite photos from Google, night-time satellite data from the National Oceanic and Atmospheric Administration, and survey data from the World Bank. Also, the team didn’t need to teach its algorithm to look for one or two specific features of poverty in the daytime photos. The machine identified a handful of features on its own by comparing daytime photos with night-time ones.
Night-time satellite images are considered a good way to pinpoint poverty based on how much light sparkles from a region. The more there is visible light, the richer the area must be is the assumption, which is why the pitch-black satellite images (see above) from North Korea – juxtaposed against the widely illuminated South Korea – are so stark.
But night photos can be tricky, says Jean. “It’s very hard to tell the difference between a poor and densely populated area and a rich but sparsely populated area,” he points out. So his algorithm uses night-time photos and World Bank survey data to corroborate the daytime photos, which are ideal to use because they have high resolution with visible features such as roofs and roads.
“Knowledge of those features enabled the authors to accurately reconstruct survey-based indicators of regional poverty, improving on results from simpler models that relied solely on nightlights or mobile phone data,” writes Dr. Blumenstock, the data scientists from Berkeley, in a column accompanying Jean’s Science paper.
While Jean’s team’s results are promising, there is more work to be done before the algorithm can be used in the field. Next, the team will try to work with images of varying resolutions – low resolutions to look for bigger structures, high resolution to see details such as roofs – to extract more information. They will look at images of regions from the past to try to predict the future economic prospects of those regions. These are only some of the possibilities of a seemingly bottomless pool of benefits of applying machine learning to this task.
“We figured it would be really cool if one day you could have models trained for the entire world,” says Jean.