Researchers at Duke University, North Carolina, and the Wildlife Conservation Society (WCS) have used AI and unmanned aerial vehicles (UAVs) to monitor large colonies of seabirds, reducing costs and human error of traditional on-the-ground methods.
The new study used a deep-learning algorithm to analyse more than 10,000 UAV images of mixed colonies of seabirds in the Falkland Islands off Argentina’s coast.
According to the researchers, the deep-learning algorithm correctly identified and counted the albatrosses with 97% accuracy and the penguins with 87%. The automated counts were within 5% of human counts about 90% of the time.
“Using drone surveys and deep learning gives us an alternative that is remarkably accurate, less disruptive and significantly easier. One person, or a small team, can do it, and the equipment you need to do it isn’t all that costly or complicated,” said Madeline C. Hayes, a remote sensing analyst at the Duke University Marine Lab, who led the study.
To conduct the new surveys, WCS scientists used an off-the-shelf consumer drone to collect more than 10,000 individual photos, which Hayes converted into a large-scale composite visual using image-processing software.
The team then analysed the image using a convolutional neural network (CNN), a type of AI that employs a deep-learning algorithm to analyse an image and differentiate and count the objects it “sees” in it, in this case two different species of sea birds. These counts were added together to create comprehensive estimates of the total number of birds found in the colonies.
“A CNN is loosely modelled on the human neural network, in that it learns from experience,” said David W. Johnston, director of the Duke Marine Robotics and Remote Sensing Lab.
“Essentially, the computer identifies different visual patterns, like those made by black-browed albatrosses or southern rockhopper penguins in sample images, and over time it learns how to identify the objects forming those patterns in other images such as our composite photo.”
Johnson added that the emerging UAV and CNN-enabled approach is widely applicable “and greatly increases our ability to monitor the size and health of seabird colonies worldwide, and the health of the marine ecosystems they inhabit.”
The scientists published their peer-reviewed findings in Ornithological Applications.
