The Defense Department’s Joint Artificial Intelligence Center is implementing automation and AI practices that could speed up the nation’s ability to rapidly respond to wildfires, the center’s Chief of Strategy and Communications Greg Allen said Wednesday.
“If you’ve ever experienced wildfires or heard about them, you know they move quite fast over the course of several days,” Allen said at Nextgov’s Emerging Technology Summit in Washington. “And keeping track of where they are and where they are headed is a real challenge for first responders in disaster situations.”
Defense has troves of sensor data, digital video data, digital infrared data and sonar data—all of which are attractive environments for machine-learning algorithms. Through this disaster-relief initiative, the agency plans to fly airborne sensors over wildfires in California and collect full-motion video data of the activity. At the same time, they are going to be automatically using a computer vision algorithm to detect which frames of the video have active wildfire.
Typically, the Federal Emergency Management Agency and other disaster-relief entities try to disseminate maps of the fire to all relevant organizations involved in the efforts once per day.
“We believe we will be able to cut that to about once per hour distributed over an app,” Allen said. “By switching to this airborne sensor, applying an AI computer vision algorithm and converting that to geolocation data that is useful for a map application we are also developing, we’ll really be able to make an impact for our users in a short time frame.”
Allen added that this is an area where his team feels like they can make an important impact. Sensor data environments generally collect “an extraordinary ..
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