AI News, Use artificial intelligence to identify, count, describe wild animals

Use artificial intelligence to identify, count, describe wild animals

Photographs that are automatically collected by motion-sensor cameras can then be automatically described by deep neural networks.

The result is a system that can automate animal identification for up to 99.3 percent of images while still performing at the same 96.6 percent accuracy rate of crowdsourced teams of human volunteers.

'This technology lets us accurately, unobtrusively and inexpensively collect wildlife data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology and animal behavior into 'big data' sciences.

They require vast amounts of training data to work well, and the data must be accurately labeled (e.g., each image being correctly tagged with which species of animal is present, how many there are, etc.).

Snapshot Serengeti has deployed a large number of 'camera traps' (motion-sensor cameras) in Tanzania that collect millions of images of animals in their natural habitat, such as lions, leopards, cheetahs and elephants.

Although projects are increasingly turning to citizen science for image classification, we're starting to see it take longer and longer to label each batch of images as the demand for volunteers grows.

Here, we wanted to demonstrate the value of the technology to the wildlife ecology community, but we expect that as more people research how to improve deep learning for this application and publish their datasets, the sky's the limit.

The Harvard Gazette

“Deep learning,” already poised to transform fields from earthquake prediction to cancer detection to self-driving cars, is about to be unleashed on a new discipline — ecology.

Researchers applied deep learning to more than 3 million photographs from the citizen-science project Snapshot Serengeti to identify, count, and describe animals in their natural habitats.

While the images can offer insight into a range of questions, from how carnivore species coexist to predator-prey dynamics, they are only useful once they have been converted into data that can be processed.

“We estimate that the deep-learning technology pipeline we describe would save more than eight years of human labeling effort for each additional 3 million images.

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