In the 16 years since Katrina, drones have gone from experimental to essential and are now smaller, cheaper and more advanced to become a routine part of any search-and-rescue mission.
Case in point: the July 2018 Carr fire, which burned 229,651 acres in northern California and killed eight people. During the fire, officials in Redding, California, deployed drones to map the devastation from the skies and to find areas where the fire was still burning.
Greg Crutsinger of drone data consulting service Scholar Farms was part of the effort. His drones took 360-degree panoramic images at the ground level, creating something akin to Google Street View for emergency responders. Crutsinger now works for private satellite company Planet.
“When I first saw the panoramic images, I was shocked at what they showed,” Crutsinger said. “Being able to pan around and see the destruction on the ground, when I was only used to mapping the area from above, had a big impact on me.”
With a close-up view, Crutsinger discovered that neighborhoods varied in the damage they’d received.
“Some looked post-apocryphal – one where a tornado touched down during the fire was just incinerated,” said Crutsinger, who shared his intelligence with the Redding Police Department, who in turn shared it with evacuated residents to prepare them for what they would see when they returned home to the destruction.
Social Media Sheds Light
In the chaos of a disaster, what responders need most but often lack is current, accurate information. Social media might be the key to getting it, according to Muhammad Yasin Kabir, a professor of computer science at the Missouri University of Science and Technology.
At the 21st IEEE International Conference on Mobile Data Management in 2020, he and his colleagues presented the prototype for a system that can help coordinate rescue operations by compiling tweets from people affected by disasters.
Called STIMULATE, the cloud-based system uses machine learning to aggregate tweets in a methodical way. When it finds a given keyword, it collects the tweet and filters it into categories that include: rescue needed, DECW (diseased, elderly, children, and pregnant women), water needed, injured, sick, and flood. The system then prioritizes rescue efforts by aggregating different factors, such as weather, GPS location, the type of help needed and the number of rescuers available
By extracting the data of natural disasters from social media posts, tools like STIMULATE can detect and track weather hazards, and determine from whom information is coming – a person affected by the disaster, a government official, the media or an emergency responder.