In a new development, experts have combined satellite imagery with Artificial Intelligence in order to detect global poverty.
With little information on the situations in developing countries, it is difficult to identify the problem. Hence, a team from Stanford University could manage a computer system that identified poor areas from five African countries.
The technique can transform efforts to identify and fight with the challenges of poverty, says the team, with Neal Jean, Marshall Burke and colleagues.
"The World Bank, which keeps the poverty data, has for a long time considered anyone who is poor to be someone who lives on below $1 a day," Dr Burke, assistant professor of Earth system science at Stanford, told the BBC.
"We traditionally collect poverty data through household surveys... we send survey enumerators around to houses and we ask lots of questions about income, consumption - what they've bought in the last year - and we use that data to construct our poverty measures."
AI examines daylight images in order to capture images such as paved roads and metal roofs that can distinguish diverse levels of poverty and prosperity in developing nations.
Scientists also used state-of-the-art to classify different indicators in the images beamed from Nigeria, Tanzania, Uganda, Rwanda and Malawi.
"If you give a computer enough data it can figure out what to look for. We trained a computer model to find things in imagery that are predictive of poverty," said Dr Burke.
"It finds things like roads, like urban areas, like farmland, it finds waterways - those are things we recognise. It also finds things we don't recognise. It finds patterns in imagery that to you or I don't really look like anything... but it's something the computer has figured out is predictive of where poor people are."
When the researchers compared traditional survey data with modern computer models, they found that there was a lot of accuracy in the computer's mapping.
"These things [that the computer model found] are surprisingly predictive of economic livelihoods in these countries," Dr Burke explained.
The plan is to cover the entire sub-Saharan Africa and later the whole of the developing world.
The results are published in the journal Science.
YouTube/Neal Jean
