Iceland, Switzerland, and Norway for years have ranked near the top of the United Nations’ annual index of countries based on indicators of well-being and quality of life.
Countries with more poverty and less access to health care and education tend to rank lower on the list, known as the Human Development Index, or HDI.
In a new study published Feb. 17 in Nature Communications , researchers used satellite imagery and machine learning to estimate HDI scores for 61,530 municipalities and counties worldwide. The results reveal that more than half the global population lives in municipalities where the development tier is different from the tier assigned at the national level, highlighting how finer-grained data can change the picture within countries.
In states and provinces that conventional assessments placed in the bottom two of five development tiers, about 8.5% of residents shifted to the top two tiers under the new municipal-level analysis. The mismatch grew to 13% when researchers estimated development for 10-by-10-kilometer grid tiles, roughly equivalent to the area of Paris.
While more granular than earlier estimates, the new data do not reveal information about individual households or neighborhoods. But they could help to guide efforts to make aid programs more effective. “We frequently target policies and programs based on these aggregate statistics, but we want to support the people who need it, not only the countries that need it,” said study co-author Solomon Hsiang , a professor of environmental social sciences in the Stanford Doerr School of Sustainability .
Since its debut in 1990, the index has transformed the way governments and aid groups think about development needs and program success beyond national economic output. “It used to be the case that the performance of countries was assessed mainly through income and economic growth. The human dimension, what was happening with people, was often lost in the policy design,” said study co-author Heriberto Tapia , who leads research and strategic partnerships for the United Nations Development Program’s Human Development Report Office .
Even proponents of the index have long recognized shortcomings, however. Relying on national averages can obscure inequality and opportunities to improve policies within countries. Data gaps persist in the poorest nations, where only about half have conducted a census in the last decade.
Satellites generate more data daily than all social media combined, Hsiang said, yet those archives remain largely untapped. In 2020, Hsiang, Tapia, and collaborators set out to determine whether this imagery could be used to improve the spatial resolution of official UN data. “Our ambition is that, thanks to these estimates that are very granular, people in different localities around the world will be able to assess what is happening with their human development following the same standard,” Tapia said.
Since the group came together, the need for insight into human development trends and drivers has only grown. Amid the pandemic recovery, extreme weather events, water scarcity, and other challenges, UN data show global human development has stalled over the past two years after decades of progress.
“We are in this particular moment when human development in the world is slowing down, and we suspect one of the reasons is because there are multiple shocks that are taking place in different parts of the world. Many of these shocks have to do with climate hazards,” said Tapia.
The researchers trained a machine learning model on satellite images of states and provinces alongside survey-based data from each country, including official HDI data from the United Nations. Because provinces are so large and have irregular shapes rather than the neat rectangles typically used in computer vision, the researchers were initially surprised their machine learning approach worked at all, said study co-lead author Jonathan Proctor , assistant professor in food and resource economics at the University of British Columbia.
The model learned to recognize relationships between HDI and certain visual features in the images, and the team then applied it to predict HDI around the world using satellite images of municipalities and counties.
“Almost all the data that we have about the world is collected from household surveys that are then aggregated up to some convenient administrative area,” said lead study author Luke Sherman, a data scientist in Hsiang’s Global Policy Laboratory at Stanford. “We showed that with satellite imagery, it’s not particularly hard to have an approximate estimate of the same variable, whether it’s school enrollment, educational attainment, or HDI, at a much finer resolution.”
Many machine learning algorithms are trained to learn complex visual patterns to identify when an image contains, say, a cat. But the systems are not designed to reveal which parts of an image contributed to a given label.
For the new research, the authors wanted to understand which features visible on Earth’s surface were influencing their model’s HDI predictions. They found that municipalities with higher road density and building density tend to have higher predicted HDI. Population is weakly associated with HDI globally, but within any given country, the more densely populated areas tend to have higher HDI. Overall, they found that built infrastructure and population density explained about a third of the variation in municipal HDI estimates. The rest remains unexplained.
In a recent pre-print , the team shared preliminary results from testing the model on more than 100 variables, finding that the technique can accurately predict a wide range of variables, including crop yields, asset ownership like cars or livestock, and electricity access. This suggests the tool could be used to increase the resolution of other types of administrative data at relatively low cost.
The researchers designed the model for ease of use at large scale, so practitioners beyond academia can harness satellite imagery to assess development gaps. “In some ways, our approach is the Toyota Camry of the remote sensing world,” Proctor said. “It doesn’t take the curves as well as a Porsche, but it gets the job done and is accessible to a broad range of people.”
Nature Communications
Global high-resolution estimates of the UN Human Development Index using satellite imagery and machine learning
17-Feb-2026