Tens of millions of birds make migratory flights for the winter each year, often flying during nighttime. They're frequently spotted by the National Weather Services' network of 159 ground-based radars, which scan the skies every 4 to 10 minutes by emitting pulses of microwaves and measuring their reflections. However, ecologists have historically struggled to make use of the resulting data sets because of their sheer magnitude, which can range up to hundreds of millions of images and hundreds of terabytes over decades. In an effort to lighten the workload, scientists at Cornell's Lab of Ornithology and the University of Massachusetts' College of Information and Computer Sciences recently investigated an AI system capable of distinguishing birds in radar images from precipitation. They say that their tool, dubbed MistNet after the fine nets ornithologists use to capture migratory songbirds, not only aids with classification tasks, but can be used to estimate birds' flying velocity and traffic rates.