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Abstract The challenges of monitoring wildlife often limit the scales and intensity of the data that can be collected. New technologies—such as remote sensing using unoccupied aircraft systems (UASs)—can collect information more quickly, over larger areas, and more frequently than is feasible using ground‐based methods. While airborne imaging is increasingly used to produce data on the location and counts of individuals, its ability to produce individual‐based demographic information is less explored. Repeat airborne imagery to generate an imagery time series provides the potential to track individuals over time to collect information beyond one‐off counts, but doing so necessitates automated approaches to handle the resulting high‐frequency large‐spatial scale imagery. We developed an automated time‐series remote sensing approach to identifying wading bird nests in the Everglades ecosystem of Florida, USA to explore the feasibility and challenges of conducting time‐series based remote sensing on mobile animals at large spatial scales. We combine a computer vision model for detecting birds in weekly UAS imagery of colonies with biology‐informed algorithmic rules to generate an automated approach that identifies likely nests. Comparing the performance of these automated approaches to human review of the same imagery shows that our primary approach identifies nests with comparable performance to human review, and that a secondary approach designed to find quick‐fail nests resulted in high false‐positive rates. We also assessed the ability of both human review and our primary algorithm to find ground‐verified nests in UAS imagery and again found comparable performance, with the exception of nests that fail quickly. Our results showed that automating nest detection, a key first step toward estimating nest success, is possible in complex environments like the Everglades and we discuss a number of challenges and possible uses for these types of approaches.