Using Computer Vision to Analyze Aerial Big Data from UAVs During Disasters by Patrick Meier.
From the post:
Recent scientific research has shown that aerial imagery captured during a single 20-minute UAV flight can take more than half-a-day to analyze. We flew several dozen flights during the World Bank’s humanitarian UAV mission in response to Cyclone Pam earlier this year. The imagery we captured would’ve taken a single expert analyst a minimum 20 full-time workdays to make sense of. In other words, aerial imagery is already a Big Data problem. So my team and I are using human computing (crowdsourcing), machine computing (artificial intelligence) and computer vision to make sense of this new Big Data source.
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Revolutionaries are chronically understaffed so Meier’s advice for natural disasters is equally applicable to disasters known as governments.
Imagine the Chicago police riot or Watts or the Rodney King riot where NGO leadership had real time data on government forces.
Meier’s book, Digital Humanitarians is a good advocacy book for the use of technology during “disasters.” It is written for non-specialists so you will have to look to other resources to build up your technical infrastructure.
PS: With the advent of cheap drones, imagine stitching together images from multiple drones with overlapping coverage. Could provide better real-time combat intelligence than more expensive options.
I first saw this in a tweet by Kirk Borne.