Ecology and AI

July 10, 2018

It's poised to transform fields from earthquake prediction to cancer detection to self-driving cars, and now scientists are unleashing the power of deep learning on a new field - ecology.

A team of researchers from Harvard, Auburn University, the University of Wyoming, the University of Oxford and the University of Minnesota demonstrated that the artificial intelligence technique can be used to identify animal images captured by motion-sensing cameras.

Using more than three million photographs from the citizen science project Snapshot Serengeti, researchers trained the system to automatically identify, count and describe animals in their natural habitats. Results showed the system was able to automate the process for up to 99.3 percent of images as accurately as human volunteers. The study is described in a June 5 paper published in the Proceedings of the National Academy of Sciences.

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

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

For years, the best method for extracting such information was to ask crowdsourced teams of human volunteers to label each image manually - a laborious and time-consuming process.

"Not only does the artificial intelligence system tell you which of 48 different species of animal is present, it also tells you how many there are and what they are doing. It will tell you if they are eating, sleeping, if babies are present, etc," said Margaret Kosmala, one of the leaders of Snapshot Serengeti and a co-author of the study. "We estimate that the deep learning technology pipeline we describe would save more than 8 years of human labeling effort for each additional 3 million images. That is a lot of valuable volunteer time that can be redeployed to help other projects."

"While there are a number of projects that rely on images captured by camera traps to understand the natural world, few are able to recruit the large numbers of volunteers needed to extract useful data," said Snapshot Serengeti founder Ali Swanson. "The result is that potentially important knowledge remains locked away, out of the reach of scientists.

"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," Swanson added. "We believe deep learning will be key in alleviating the bottleneck for camera trap projects: the effort of converting images into usable data."

A form of computational intelligence loosely inspired by how animal brains see and understand the world, deep learning relies on training neural networks using vast amounts of data. For that process to work, though, the training data must be properly labeled.

"When I told (senior author) Jeff Clune we had 3.2 million labeled images, he stopped in his tracks," said Craig Packer, who heads the Snapshot Serengeti project. "Our citizen scientists have done phenomenal work, but we needed to speed up the process to handle ever greater amounts of data. The deep learning algorithm is amazing and far surpassed my expectations. This is a game changer for wildlife ecology."

Going forward, first-author Mohammad Sadegh Norouzzadeh believes deep learning alogrithms will continue to improve and hopes to see similar systems applied to other ecological data sets.

"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," he said. "It is exciting to think of all the different ways this technology can help with our important scientific and conservation missions."

"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," said Jeff Clune, the Harris Associate Professor at the University of Wyoming and a Senior Research Manager at Uber's Artificial Intelligence Labs, and the senior author on the paper. "This will dramatically improve our ability to both study and conserve wildlife and precious ecosystems."
-end-
The paper was written by Clune, his PhD student Mohammad Sadegh Norouzzadeh, his former PhD student Anh Nguyen (now at Auburn University), Margaret Kosmala (Harvard University), Ali Swanson (University of Oxford), and Meredith Palmer and Craig Packer (both from the University of Minnesota).

This research was supported with funding from the National Science Foundation.

Harvard University

Related Technology Articles from Brightsurf:

December issue SLAS Technology features 'advances in technology to address COVID-19'
The December issue of SLAS Technology is a special collection featuring the cover article, ''Advances in Technology to Address COVID-19'' by editors Edward Kai-Hua Chow, Ph.D., (National University of Singapore), Pak Kin Wong, Ph.D., (The Pennsylvania State University, PA, USA) and Xianting Ding, Ph.D., (Shanghai Jiao Tong University, Shanghai, China).

October issue SLAS Technology now available
The October issue of SLAS Technology features the cover article, 'Role of Digital Microfl-uidics in Enabling Access to Laboratory Automation and Making Biology Programmable' by Varun B.

Robot technology for everyone or only for the average person?
Robot technology is being used more and more in health rehabilitation and in working life.

Novel biomarker technology for cancer diagnostics
A new way of identifying cancer biomarkers has been developed by researchers at Lund University in Sweden.

Technology innovation for neurology
TU Graz researcher Francesco Greco has developed ultra-light tattoo electrodes that are hardly noticeable on the skin and make long-term measurements of brain activity cheaper and easier.

April's SLAS Technology is now available
April's Edition of SLAS Technology Features Cover Article, 'CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence'.

Technology in higher education: learning with it instead of from it
Technology has shifted the way that professors teach students in higher education.

Post-lithium technology
Next-generation batteries will probably see the replacement of lithium ions by more abundant and environmentally benign alkali metal or multivalent ions.

Rethinking the role of technology in the classroom
Introducing tablets and laptops to the classroom has certain educational virtues, according to Annahita Ball, an assistant professor in the University at Buffalo School of Social Work, but her research suggests that tech has its limitations as well.

The science and technology of FAST
The Five hundred-meter Aperture Spherical radio Telescope (FAST), located in a radio quiet zone, with the targets (e.g., radio pulsars and neutron stars, galactic and extragalactic 21-cm HI emission).

Read More: Technology News and Technology Current Events
Brightsurf.com is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com.