From pictures to numbers: Multi-species seabird surveys using drone imagery and neural networks

Seabirds are among the most threatened avian taxa globally, with over half of all species in decline. Accurate population estimates are essential for tracking trends and informing conservation, yet traditional survey methods are limited by logistical challenges, high costs, and the potential for wildlife disturbance, particularly in remote coastal areas. Unoccupied aerial vehicles (UAVs or drones) offer an efficient and low-disturbance alternative, but the vast volumes of imagery they produce are often labour-intensive to analyse. In this study, we combined drone imagery with deep learning techniques to estimate colony size and abundance of surface-nesting seabirds based on counts of visible individuals. High-resolution aerial imagery was collected from 163 colonies along the southern and central Norwegian coastline over three breeding seasons (2022–2024), covering a total of 7.67 km 2 . A convolutional neural network (Faster R-CNN with ResNet-101 backbone) was trained on 131 annotated orthomosaics and evaluated on 32 additional annotated orthomosaics from geographically distinct colonies. Across all data, 23,062 individual seabirds were annotated. Colonies hosted an average of 141.5 ± 193.9 individuals and 4.1 ± 2.3 focal species per site. At a confidence threshold of 0.7, the model achieved a detection rate of 87.5 % and a macro F1-score of 0.88. It performed well across multiple focal species, including terns, gulls, and cormorants, and remained robust in mixed-species colonies. Most errors involved false negatives or confusion among visually similar species. Our results demonstrate the potential for deep learning models to support efficient, scalable, and low-disturbance seabird monitoring across diverse habitats, reducing manual annotation effort and informing conservation practice. • Cloud-based pipeline for automated seabird detection from drone imagery. • Surveys covered 163 colonies and more than 23,000 annotated seabirds. • Deep learning model trained to detect and classify multiple seabird species. • Robust performance in mixed-species and visually complex seabird colonies. • Approach reduces manual effort and enables scalable seabird monitoring.