A new technique using artificial intelligence to read satellite images could aid efforts to eradicate global poverty by indicating where help is needed most, a team of U.S. researchers said on Thursday. The method would assist governments and charities trying to fight poverty but lacking precise and reliable information on where poor people are living and what they need, the researchers based at Stanford University in California said. Eradicating extreme poverty, measured as people living on less than 1.25 U.S. a day, by 2030 is among the sustainable development goals adopted by United Nations member states last year. A team of computer scientists and satellite experts created a self-updating world map to locate poverty, said Marshall Burke, assistant professor in Stanford's Department of Earth System Science. It uses a computer algorithm that recognises signs of poverty through a process called machine learning, a type of artificial intelligence, he said.

By combining satellite data and sophisticated machine learning, researchers have developed a technique to estimate household consumption and income. Such data is particularly difficult to obtain in poorer countries, yet it is critical for informing research and policy, and for efforts including resource allocation and targeted intervention in these developing nations. The African continent provides a particularly striking example of limited insights into economic wellbeing. According to World Bank data from 2000 to 2010, 39 out of 59 African countries conducted less than two surveys substantial enough to result in poverty measures. Surveys are costly, infrequent, and cannot always reach countries or regions within countries, for instance, due to armed conflict.

Organizations often conduct door-to-door surveys to identify people living in poverty, but the downside is that these surveys are often time-consuming and expensive. Indeed, locating impoverished environments is still a challenging process for researchers, and the availability of accurate information is still lacking. Now, in a new study, scientists from Stanford University propose a more reliable method to map poverty in areas previously void of data -- by combining satellite images and making use of machine learning. Led by Stanford computer science doctoral student Neal Jean, researchers sought to determine whether the combination of high-satellite imagery and machine learning -- the science of designing algorithms that learn from data -- could predict estimates of areas where impoverished people lived. Specifically, they extracted information about poverty from these satellite images, and built upon previous machine learning algorithms to detect impoverished areas across five countries in Africa.

Researchers have combined satellite imagery with AI to predict areas of poverty across the world. There's little reliable data on local incomes in developing countries, which hampers efforts to tackle the problem. A team from Stanford University were able to train a computer system to identify impoverished areas from satellite and survey data in five African countries. Neal Jean, Marshall Burke and colleagues say the technique could transform efforts to track and target poverty in developing countries. "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's Science in Action programme.

LONDON – A new technique using artificial intelligence to read satellite images could aid efforts to eradicate global poverty by indicating where help is needed most, a team of U.S. researchers said on Thursday. The method would assist governments and charities trying to fight poverty but lacking precise and reliable information on where poor people are living and what they need, the researchers based at Stanford University in California said. Eradicating extreme poverty, measured as people living on less than 1.25 U.S. a day, by 2030 is among the sustainable development goals adopted by United Nations member states last year. A team of computer scientists and satellite experts created a self-updating world map to locate poverty, said Marshall Burke, assistant professor in Stanford's Department of Earth System Science. It uses a computer algorithm that recognizes signs of poverty through a process called machine learning, a type of artificial intelligence, he said.

Getting aid to impoverished Africans is hard enough, what with blockades of bureaucracy and red tape. But in many African countries, bad data, or a lack of it, makes distributing funds even more troublesome. "Fighting poverty has always been this shining goal of the modern world," Neal Jean, a doctoral student in computer science at Stanford University's School of Engineering, told me. "It's the number one priority for the United Nation's 2030 Agenda for Sustainable Development, but the major challenge is that there's not enough reliable data. It's really hard to help impoverished people when you don't know where they are."

"Seven hundred years after glasses were invented there are still 2.5 billion people in the world with poor vision and no access to vision correction," says Hong Kong philanthropist James Chen. Chairman of his family's Nigeria-based manufacturing company, Wahum Group, Chen is funding a contest called the Clearly Vision Prize that will award a total of 250,000 to projects that improve eyesight, especially in poor countries. Thirty-six semifinalists were announced this week (the five winners will be awarded September 15). Among the contenders: 3D printed eyeglass frames, drones that deliver medical supplies, and several smartphone-based technologies. Some of the smartphones help nonexperts test vision, and one uses artificial intelligence to "see" for blind people. The Clearly Vision semifinalists represent just a sampling of the smartphone projects fighting vision loss, a growing field that is bringing critical care to remote regions far from hospitals and doctors offices.

SIRTE, Libya Suicide bombings against Libyan forces battling to oust Islamic State from their former North African stronghold of Sirte killed at least 12 fighters and wounded about 60 there on Thursday, a hospital spokesman said.

LONDON (Thomson Reuters Foundation) - A new technique using artificial intelligence to read satellite images could aid efforts to eradicate global poverty by indicating where help is needed most, a team of U.S. researchers said on Thursday. The method would assist governments and charities trying to fight poverty but lacking precise and reliable information on where poor people are living and what they need, the researchers based at Stanford University in California said. Eradicating extreme poverty, measured as people living on less than 1.25 U.S. a day, by 2030 is among the sustainable development goals adopted by United Nations member states last year. A team of computer scientists and satellite experts created a self-updating world map to locate poverty, said Marshall Burke, assistant professor in Stanford's Department of Earth System Science. It uses a computer algorithm that recognizes signs of poverty through a process called machine learning, a type of artificial intelligence, he said.

Satellites staring down at Earth can see a lot from their posts in space. Powerful eyes in the sky can pick out homes, natural formations, the pyramids and even small cars driving on roads. And now, scientists are using the wealth of data collected by these satellites to solve major problems on Earth. A new study published in the journal Science this week uses machine learning -- a type of artificial intelligence that lets computer algorithms change when given new data -- coupled with satellite imagery to map poverty in Nigeria, Uganda, Tanzania, Rwanda and Malawi. This new technique could help revolutionize the way groups find impoverished areas and eventually get relief to people living in those specific parts of the world.