How AI Can Protect the World’s Woods from Deforestation

by Isha Salian

For weeks, the Amazon rainforest has been burning at a startling rate. Tens of thousands of fires have been recorded this year — largely started by humans clearing land for logging, ranching or mining.

Weak regulations and the insufficient levels of forest monitoring personnel around the globe are no match for an illegal timber market worth up to $152 billion. Around a fifth of global carbon dioxide emissions come from deforestation.

But AI can give officials ears all over the forest, listening for chainsaws and unauthorized vehicles — warning signs of illegal logging in progress. Outland Analytics, a member of the NVIDIA Inception virtual accelerator, has developed a tree-mounted device that uses audio recognition algorithms to detect these signals and alert forest rangers.

“We have a dire law enforcement shortage,” said Elliot Richards, 20-year-old CEO of the Philadelphia-based startup, which began as a high school engineering project and is now a six-person company. “It’s a lot of not being in the right place at the right time.”

For every 300,000 acres of land managed by the U.S. Forest Service — an area equivalent to nearly 500 square miles — there’s just one law enforcement officer patrolling for illicit activity. A network of warning systems could help understaffed forest monitoring agencies worldwide better track and prevent illicit logging before it’s too late.

The AI algorithms behind Outland Analytics’ system are trained using NVIDIA GPUs, including a V100 Tensor Core GPU in the IBM Cloud. The company is working with the New York State Department of Environmental Conservation for field testing and plans to launch a paid pilot program in the fall.

If a Tree Falls in the Forest, AI Will Hear It

outland analytics device mounted on a tree
AI Speaks for the Trees: Outland Analytics edge devices can be mounted to a tree to listen for chainsaws and unauthorized vehicles.

Not every high school project turns into a full-fledged startup. But that’s how Outland Analytics got going, inspired by Richards and co-founder Edward Buckler’s love of nature and interest in land management.

Now undergrads at Drexel University and Stony Brook University, respectively, the founders started working on the company three years ago with the goal of improving forest protection.

While some organizations use satellite imagery or trail cameras that might provide notifications to forest rangers, those methods typically don’t provide immediate results — and it’s near impossible to identify individuals from the footage. Low-latency AI models that analyze audio could shorten response times, giving rangers minute-to-minute visibility into large areas of forest.

Using the TensorFlow deep learning framework, the team trained their AI algorithms with around 100 hours of audio from field recordings and publicly available data.

“GPUs in the cloud are nice because they’re preconfigured for you,” said Buckler. “We were blown away by how easy it was to tell a V100 on IBM Cloud to train our model, come back a few hours later and it’s all good to go.”

Buckler and Richards built a cellular-connected edge device about the size of a small backpack, topped with a solar panel and antenna. Strapped to a tree, a single device can monitor up to 150 acres of forest, collecting sound signals and sending them to the cloud for analysis.

If the neural network detects a chainsaw or unauthorized vehicle, it’ll contact officials through an email to a dispatch center or a text message to an individual ranger. Authorities can then head to the scene to catch potential environmental crimes in progress.

The low-maintenance device can be mounted at any height on a tree and is charged by solar power — though it can last a few days without sun. It’s so far been tested in the Adirondack and Catskill mountain ranges.

“The forests have the odds against them for protection,” said Richards. “We want to bolster the presence of specialized police forces by enabling them to respond to in-progress crimes.”