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. Today, aid workers conduct surveys on the ground in poor areas, but that kind of mapping is incredibly time-consuming and even expensive. Developing a faster, more automated system could save time and money. Plus, it could help get aid to those who need it more quickly. "I think the goal is to understand the world much better, in particular the world where a lot of poor people live. And that includes understanding their livelihoods in terms of their sources of income, how their agriculture is performing, how different sectors of the economy perform, and, more specifically, what actually is effective at improving conditions," co-author of the study David Lobell told Mashable in an interview. "All of that is really difficult if you don't have good measurements." But creating the tools used for the study wasn't exactly simple. The authors of the new study needed to figure out a way to predict poor communities without clearly knowing if they are poor or not. "There are few places in the world where we can tell the computer with certainty whether the people living there are rich or poor," study co-author Neal Jean, said in a statement. "This makes it hard to extract useful information from the huge amount of daytime satellite imagery that's available." To solve this issue, the scientists used day and nighttime satellite imagery to see which parts of the countries are brighter than others. Usually, areas with less artificial light are less developed. Therefore, the scientists mapped those dimmer parts of the map at night with daytime photos of the same areas, allowing the computer to pick out patterns — like road conditions or metal roofs versus thatched roofs — that indicate a less-developed and possibly poorer region. According to the study, this method is 81 percent more effective at predicting poverty in an area under the poverty line than a method using nighttime imagery alone. The authors also think that the algorithm may be effective when used in multiple countries and regions.