At last count, the earthquake that struck Nepal on April 25 and the large aftershock that followed three weeks later had claimed more than 8,500 lives, making it the largest disaster in the country’s history. It is also a watershed in another way. It was the first time artificial intelligence was used so extensively in relief efforts to tackle the overwhelming amount of information generated by mobile phones, satellites, and social media, to name just a few, to help aid workers locate victims, identify relief needs, and help aid workers navigate dangerous terrain.
One of the most crucial first steps in disaster relief is getting a picture of what the new terrain looks like—what roads are blocked and what buildings have crumbled. Shortly after the quake, the UN Office of Coordination of Humanitariaon Affairs activated the Digital Humanitarian Network to make sense of the big data generated by the disaster. The network, which I co-founded with OCHA in 2012 in response to the 2010 earthquake in Haiti, serves as the official interface between hundreds of established humanitarian organizations and tech-savvy digital volunteer networks from across the globe. These volunteers provide aid groups like the UN, Red Cross, and the World Bank with the surge capacity, knowledge, and technology needed to parse millions of pieces of crisis-related data and find the most relevant ones.
One of the most crucial first steps in disaster relief is getting a picture of what the new terrain looks like—what roads are blocked and what buildings have crumbled—and the quickest and safest way an aid worker can get from point A to point B. Digital maps that provide real-time information register change as the disaster unfolds, allowing humanitarian workers to operate safely and accurately.
In Nepal, OCHA asked the Network to identify and map all tweets related to urgent needs, infrastructure damage, and response efforts. They also wanted
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