A new study using satellite images and machine learning plans to map poverty from space in an effort to “fix the world’s problems.” Satellite imagery can be less dangerous, slow and expensive than gathering the data on the ground. BBC reports: “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. The latest study looked at daylight images that capture features such as paved roads and metal roofs — markers that can help distinguish different levels of economic wellbeing in developing countries. They then used a sophisticated computer model to categorize the various indicators in daytime satellite images of 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 recognize. It also finds things we don’t recognize. 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.’ The researchers used imagery from countries for which survey data were available to validate the computer model’s findings.” The results of the study are published in the journal Science.
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