The best way to predict poverty is by combining satellite images with machine learning

The first step to eliminating poverty is identifying the regions most plagued it. In 2015, the United Nations Sustainable Development Goals set forth 17 global missions. One of the most ambitious is to eliminate poverty by 2030. But, currently, on-the-ground economic measures are sparse and sometimes unreliable in poorer countries, which lack resources to collect accurate data. Much of the data from Sub-Saharan African countries is not well-documented, edited, or released to the public domain, according to the World Bank. “How are we going to know if we’ve eliminated poverty if we don’t collect data?” says Marshall Burke, an assistant professor in Stanford University’s Department of Earth System Science. “It’s like setting a weight loss goal but not having a scale to know if you’ve made any progress.” One solution…


Link to Full Article: The best way to predict poverty is by combining satellite images with machine learning