In a first, UMass Amherst, Cornell use AI to mine big migration data on massive scale
Date: August 28, 2019
Source: University of Massachusetts at Amherst
On many evenings during spring and fall migration, tens of millions of birds take flight at sunset and pass over our heads, unseen in the night sky. Though these flights have been recorded for decades by the National Weather Services' network of constantly scanning weather radars, until recently these data have been mostly out of reach for bird researchers.
That's because the sheer magnitude of information and lack of tools to analyze it made only limited studies possible, says artificial intelligence (AI) researcher Dan Sheldon at the University of Massachusetts Amherst.
Ornithologists and ecologists with the time and expertise to analyze individual radar images could clearly see patterns that allowed them to discriminate precipitation from birds and study migration, he adds. But the massive amount of information ¬- over 200 million images and hundreds of terabytes of data -- significantly limited their ability to sample enough nights, over enough years and in enough locations to be useful in characterizing, let alone tracking, seasonal, continent-wide migrations, he explains.
Clearly, a machine learning system was needed, Sheldon notes, "to remove the rain and keep the birds."
Now, with colleagues from the Cornell Lab of Ornithology and others, senior authors Sheldon and Subhransu Maji and lead author Tsung-Yu Lin at UMass's College of Information and Computer Sciences unveil their new tool "MistNet." In Sheldon's words, it's the "latest and greatest in machine learning" to extract bird data from the radar record and to take advantage of the treasure trove of bird migration information in the decades-long radar data archives. The tool's name refers to the fine, almost invisible, "mist nets" that ornithologists use to capture migratory songbirds.