AI-Assisted System to Detect and Track Communal Roosts in Weather Radar Data

We have developed an AI-assisted system to annotate communal roosts of birds and bats in weather radar data. This system comprises detection, tracking, confounder filtering, and human screening components. We have deployed this system to gather information on swallows from 612,786 scans taken from 12 radar stations around the Great Lakes over 21 years. The 15,628 annotated roost signatures have uncovered population trends and phenological shifts in swallows and martins. These species are rapidly declining aerial insectivores, and the data gathered has facilitated crucial sustainability analyses. While human screening is still required with the deployed system, we estimate that the screening process is approximately 7$\times$ faster than manual annotation. Furthermore, we found that incorporating temporal signals enhances the deployed detector's performance, increasing the mean average precision (mAP) from 48\% to 56\%. Our ongoing work aims to expand the analysis to bird and bat roosts at a continental scale.