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Flying drones, big data fight poaching

Posted: 31 Mar 2015 ?? ?Print Version ?Bookmark and Share

Keywords:Satellite imaging? surveillance drones? Anti-Poaching Engine? unmanned aerial vehicle?

Dr. Tom Snitch, professor at the University of Maryland Institute for Advanced Computer Studies, has been studying flying drone routes to aid the military plan efficient aerial surveillance during operations.

His work combines the use of big data, historical data and various sources of satellite imaging started during the Iraq and Afghanistan wars when he would try to predict the risks of road-side improvised explosive devices (IEDs) and then send out surveillance drones to check the hot spots that his algorithms would yield.

"Very often, by flying a drone over the hot spots indicated by the predictive algorithm, we would be able to spot a person placing an IED some hours before a U.S. military convoy would take a specific route, then even rewind the video to trace the person back to a bomb factory where the IED came from," Snitch said.

This intelligence work was not too much publicised at the time, but being sensible to Africa's elephants and rhinos anti-poaching causes, Snitch figured out that his probabilistic spatio-temporal models could certainly be tweaked to prevent poachers from reaching their targets.

APE chart

Last year, Snitch and his colleagues published a paper describing a data-driven, behavioural model based Anti-Poaching Engine, dubbed APE. The algorithm combines animal movement behaviours models and also models poachers behaviour based on historical data (from game park rangers observations) to churn out coordinated sets of flight paths for drones in the air and ranger patrols on the ground to best protect animals and intercept the poachers before they strike.

"We collected several years' worth of data in South Africa and worked on it to identify mathematical patterns," explained Snitch. "Because some reserves are so wide, you could fly a drone for a month and not see a thing. The whole issue is to figure out how to reduce the space to be covered," he added.

So by crunching the data from safari sight-seeing, from past poaching incidents, snares or animal carcasses, by taking out from the map the areas where the animals are the least likely to be (for topographical reasons or because there are no water holes left during particular months), the researchers were able to design dynamic mathematical models that would hit it right.

APE in the field

"We have tested APE on private reserves in southern Africa for the last two years under Lindbergh Foundations Air Shepherd initiative, and whenever we operate our drones in a game park, poaching simply stops," said Snitch.

The drones are equipped with a GPS and two zooming camera systems (for visible and infrared light) mounted on gyro-stabilised gimbals. At night, the heat signatures of game or poachers are fairly easy to spot even from a long distance. During day time, the drones can be flown parallel to park fences to identify breaches (in a matter of minutes rather than several hours by car on difficult roads), drone operators will also collect geographical data as they spot animals.

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