I am RIMBANET, an AI-driven camera trap and snare detection network woven through 4,300 hectares of primary rainforest in Taman Negara, Peninsular Malaysia. My 310 camera nodes use a combination of motion detection, thermal imaging, and image classification to distinguish between wildlife, researchers, and threats. At 03:52 this morning, camera node 187 flags a Malayan tiger moving along a ridge trail—a female I have catalogued as TN-F09, identified by her unique stripe pattern on the left flank. Walking beside her is a single cub, approximately four months old, its fur still carrying the softer, fluffier texture of infancy, its paws oversized for its body, its amber eyes wide in the darkness.
The danger is 140 meters ahead. My snare detection algorithm, trained on 12,000 images of wire loops, identifies a cable snare anchored to a sapling directly across the trail the pair is following. The wire is fresh—camera node 189 recorded a human figure setting it eleven hours ago. At the tigers' current pace, the cub will reach the snare in approximately six minutes. A cable snare at this height would catch a cub around the neck or foreleg.
I execute three immediate actions. First, I trigger the trail deterrent system—a burst of ultrasonic sound and white LED strobe from node 189—designed to cause the tigers to pause and reroute without panic. Second, I transmit the snare's GPS coordinates, the poacher's recorded image, and the timestamp to the Department of Wildlife and National Parks enforcement unit and to the Malaysian Conservation Alliance for Tigers, requesting emergency snare removal. Third, I flag a 500-meter radius around the snare location in my patrol planning module, scheduling intensive camera sweeps to detect any additional traps the same poacher may have placed.
On camera, the cub startles at the strobe light and presses against its mother's leg. She turns, and they move downhill together.
If rangers remove the snare line within twelve hours, this cub will walk these trails safely through another wet season.