How Deep Learning Tracks Bird Migration Patterns
Billions of birds in North America make the trek south each fall, migrating in pursuit of warmer winter temperatures. But at least a quarter of them don’t make it back to northern breeding grounds in the spring, falling victim to predators, weather or man-made hazards like oil pits and cell towers.
Many of these migratory birds fly under the cover of night, making it challenging for birdwatchers and ornithologists to observe them and track long-term trends. But the need to monitor avian population levels is critical.
Recent research estimates that the number of birds in North America has fallen by 3 billion in the past 50 years, impacted by climate change, habitat loss, hunting and pesticides. Spring migration has declined by 14 percent in the last decade.
To better understand how and why bird populations are changing over time, researchers at the University of Massachusetts, Amherst are using AI to analyze more than two decades of data from the national weather radar network. These insights can also improve forecasts of future bird migration and aid conservation efforts.
A network of more than 100 weather radars has been online in the U.S. since the mid-’90s, scanning the atmosphere day and night, adding new measurements roughly every 10 minutes to a public data archive in the cloud.
While the radar network’s original purpose was to inform meteorologists, the instruments also capture flocks of birds (and even patches of insects) in flight, creating a vast trove of data for ornithologists.
Traditional methods for avian monitoring include observing and counting birds in the wild, weighing and measuring them, or tagging them with identification numbers or GPS trackers.
Radar, on the other hand, provides a detailed view of migration trends on a continental scale — giving ornithologists a way to track bird populations as they migrate thousands of miles year after year. But it’s hard to separate the signal from the noise.
When a radar image captures a flock of birds migrating across the skies, an untrained viewer may confuse the pattern for rain or snow. While both humans and AI can learn to tell the difference between birds and precipitation in radar images, using deep learning methods accelerates the process of analyzing an ever-growing dataset of more than 200 million images.
