Cornell researchers to use Machine Learning to fight hunger and poverty

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With one or other battle, war or conflict raging in many parts of world, what this world needs is peace. And as Nobel prize winner and father of green revolution, Dr.Norman Borlaug famously said, "There cannot be any peace on hungry stomach". If people are well fed and hence happy, they are less likely to engage in conflicts. A group of researchers from Cornell University would use ML techniques to analyse food and market conditions, to predict poverty and malnutrition in poorest region of the planet. The method would use available satellite data to measure solar induced chlorophyll fluorescence (SIF).


New algorithm can detect poverty- from space - Redorbit

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Attempting to locate and assist people living in impoverished parts of the world could be made easier by using satellite imagery and machine learning algorithms, according to a new study led by researchers at Stanford University and published in the journal Science. Traditionally, international aid group perform door-to-door surveys to record data on local incomes in developing nations, but as study author Marshall Burke of the Stanford Institute for Economic Policy Research explained, these methods can be expensive and time consuming. They believe they've found a more efficient alternative. "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," Burke told BBC News.


Researchers tap NASA satellites to predict Malaria

Daily Mail - Science & tech

A group of researchers is using data from NASA satellites to predict outbreaks of malaria, which is difficult to track and control because it spreads mostly in remote areas. Specifically, they're combing NASA weather satellites with the Land Data Assimilation System (LDAS), a land-surface modeling system that can track and predict temperatures, rainfall levels, soil moisture content, and vegetation. They're working to create models that will indicate where mosquitoes - which carry the deadly disease - are, as well as predict outbreaks 12 weeks in advance and pinpoint them down to the household. A group of researchers is using data from NASA satellites to predict outbreaks of Malaria, which is difficult to track and control because it spreads mostly in remote areas. Malaria is one of the deadliest diseases to humans - it causes high fever, headaches, and chills and has a high fatality rate.


How to fight global poverty from space

AITopics Original Links

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.


How to fight global poverty from space

#artificialintelligence

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.