  The US weather radar archive holds detailed information about biological
phenomena in the atmosphere over the last 20 years. Communally roosting birds
congregate in large numbers at nighttime roosting locations, and their morning
exodus from the roost is often visible as a distinctive pattern in radar
images. This paper describes a machine learning system to detect and track
roost signatures in weather radar data. A significant challenge is that labels
were collected opportunistically from previous research studies and there are
systematic differences in labeling style. We contribute a latent variable model
and EM algorithm to learn a detection model together with models of labeling
styles for individual annotators. By properly accounting for these variations
we learn a significantly more accurate detector. The resulting system detects
previously unknown roosting locations and provides comprehensive
spatio-temporal data about roosts across the US. This data will provide
biologists important information about the poorly understood phenomena of
broad-scale habitat use and movements of communally roosting birds during the
non-breeding season.
