TEHRAN,Young Journalists Club(YJC) -Weather radar can offer a spotty view of the phenomenon, but to track nighttime migrations with greater accuracy and reliability, a group of researchers at the University of Massachusetts at Amherst turned to artificial intelligence.
Scientists designed a machine-learning algorithm to analyze weather radar images and differentiate migrating birds from precipitation. The algorithm replicates the power of neural networks to analyze and classify radar images.
Researchers used the new artificial intelligence program to survey decades-long radar data sets, revealing seasonal and continent-wide migration patterns.
The team dubbed the program MistNet and described its capabilities in a paper published this week in the journal Methods in Ecology and Evolution.
"This is a really important advance. Our results are excellent compared with humans working by hand," Amherst artificial intelligence researcher Dan Sheldon said in a news release. "It allows us to go from limited 20th-century insights to 21st-century knowledge and conservation action." He and co-authors point out, "Deep learning has revolutionized the ability of computers to mimic humans in solving similar recognition tasks for images, video and audio."
Scientists tested their system against multiple observational studies to verify its accuracy. The new technology -- which scientists hope to eventually integrate with other technologies, like eBird, animal tracking devices and earth observation instruments -- could help researchers identify important stopping points for migrating species, information that could be used to plan more effective conservation efforts.
"We hope MistNet will enable a range of science and conservation applications. For example, we see in many places that a large amount of migration is concentrated on a few nights of the season," Sheldon said. "Knowing this, maybe we could aid birds by turning off skyscraper lights on those nights."