This AI Hunts Poachers | TechNewsSoft.com
Every year, poachers kill about 27,000 African elephants – a staggering 8% of the population. If current trends continue, these beautiful animals could disappear within a decade.
Predictive Patrols: Computer scientist Fei Fang in Carnegie Mellon shows a tiger trap found in a wildlife preserve in northeastern China. Fang and Milind Tambe of the University of Southern California have developed a machine learning algorithm to predict where poachers are most active.
For the PAWS team, field trials have highlighted an important reality of maintaining order in wilderness: the world is not flat. When the team started working in Malaysia, says Fang, they did not take into account the densely forested mountainous terrain. “In our first model, we took a map, divided the entire area into grid cells, plotted a line on the grid and said,” Patrols, please follow this line, “she recalls. . “We had Skype calls with them, and they said,” No, no, no, it’s not going to work. “We did not understand.”
This is only when the PAWS team visited the Malaysian reserve that they got it. “We walked with the rangers, and it took us about eight hours to do a few kilometers,” says Fang. A further refinement of PAWS takes into account geographical features that are easy to walk, such as ridges, riverbeds, and old forest trails. “We built a virtual map for the conservation area and then traced the routes according to the map.” Patrollers following the new roads found “all kinds of signs of animal and human activity,” says Fang.
At the time of going to press, Fang was in the middle of a three-month field trial of PAWS in northeastern China with the World Wildlife Fund, where the most pet worrying is the Siberian tiger. Fang says that one improvement they are working on is helping the rangers make decisions on patrol. “They can see footprints and tree marks, which indicate the direction that the poachers are heading,” she says. “And they must decide, Should I hunt poachers, what is the best strategy for changing plans if they see new information?”
Tambe and Fang also collaborate with a wildlife conservation service called Air Shepherd that uses drones equipped with infrared cameras to search for poachers at night. Their AI-based video analytics system automates what is otherwise a tedious and challenging task for humans: reviewing hours and hours of black-and-white grainy sequences and alerting rangers when Illegal activity is detected.
The next step for PAWS is to make it available to other NGOs, ideally by incorporating the algorithm into existing tools, such as Cybertracker and the SMART system. “We will probably never stop poaching completely,” Plumptre says. “But we can lower it to a lower level, so people do not decline.”
The AI is generally applied to the problems of modern technology, notes Tambe, but this work is different. “We use AI to save the natural world – these beautiful landscapes and animals that we hope will not go away,” he says. “These are important treasures.”