How London’s zoo is using Cloud AutoML to prevent poaching

How London’s zoo is using Cloud AutoML to prevent poaching with automated image-tagging

The Zoological Society of London (ZSL) has turned to Google’s new Cloud AutoML platform to track wildlife, by automatically analysing millions of images captured by cameras in the wild.

These cameras help ZSL to conserve different species of wildlife by identifying their movements and identifying potential poachers.

The motion-triggered camera traps use heat sensors to identify when wildlife or humans move past, and produce vast amounts of data that quickly needs to be tagged.

Reporting on that data typically takes up to nine months, by which time the animal movements and ZSL strategies may well have changed.

“You need that information much quicker in the fight to conserve wildlife or stop poaching,” Sophie Maxwell, the conservation technology lead at ZSL told Computerworld UK.

Google’s Cloud AutoML uses artificial intelligence and machine learning algorithms to cut those nine months down to an instant. The platform helps organisations with limited machine learning expertise, such as ZSL, to build their own high-quality custom models using advanced machine learning techniques and tools.

Why AutoML?

Before turning to Google, ZSL conservationists had to manually tag these hundreds of thousands of images, a painstaking and time-consuming process. Cloud AutoML can do all this automatically.

It stood out from its competitors as it could use ZSL’s bespoke models and train them on the charity’s vast existing dataset.

Maxwell can’t release results on the trials but says the potential has been proven by early analysis of the images. Cloud AutoML has proven especially impressive on the tricky task of recognising species subsets.

“That’s when this AutoML is much better than other image recognition solutions that are out there, because you’re able to do these bespoke models based on your bespoke species set and your bespoke location,” she says.

“It’s not just more top level, so you can train it on your existing dataset and then you can start to define subspecies within that group, whether that’s an impala or an oryx within this antelope family for example.”

Read more

0 0 votes
Article Rating
Notify of
Inline Feedbacks
View all comments
Would love your thoughts, please comment.x