Crisis responders need to know the extent of a natural disaster, what aid is required and where they need to get to as quickly as possible — this is what’s known as “situation awareness.” With the proliferation of mass media, a lot of data is now generated from the disaster zone via photographs, tweets, news reports and the like. With the addition of first responder reports and satellite images of the disaster area, there is a vast amount of relevant unstructured data available for situation awareness. A crisis response team will be overwhelmed by his data deluge — perhaps made even worse by reports written in languages they don’t understand. But the data is also hard to interpret by computers alone, as it’s difficult to find meaningful patterns in such a large amount of unstructured data, let alone understand the complex human problems that described within it. Experts say that joint humans-computers teams would be the best way to deal with voluminous, but unstructured, data.

Over the past five years, researchers from Oxford University have been working on a collaborative project called ORCHID to develop new ways for humans and computers to work together.

This week, the team from Oxford joined their academic collaborators from the University of Southampton and University of Nottingham at the Royal Academy of Engineering to showcase their work. Oxford Science blog spoke to Dr. Steven Reece, a Senior Research Fellow at the University’s Pattern Analysis and Machine Learning Research Group, to find out how the Oxford team has been using its research to help disaster response teams.