Automated AI image recognition tools improve the efficiency of aerial wildlife counts: A multi-species case study on breeding seabirds and pinnipeds at the sub-Antarctic Bounty Islands

Abstract Accurate monitoring of populations is essential for conservation management, including for vulnerable seabirds. Yet traditional ground-based surveys are logistically challenging and time-consuming, especially in remote environments such as the sub-Antarctic islands. Advances in aerial imagery and artificial intelligence (AI) offer opportunities to improve the efficiency and repeatability of population surveys. In this study, we evaluate an AI-based approach for counting Salvin’s albatross from high-resolution aerial imagery collected using a piloted fixed-wing aircraft at the Bounty Islands, New Zealand. Imagery acquired during a single-day survey was processed to create orthomosaic images, which were previously analysed using manual counts by an experienced observer. We applied an automated detection and counting model based on a Faster R-CNN architecture with Slicing-Aided Hyper-Inference, and compared AI-derived counts with original human counts in terms of accuracy, consistency, and processing time. The AI achieved an initial F1 score of 92.8% for albatross detection and produced counts within 3% of the manual results, while reducing processing time from approximately 66 hours to just over four minutes. The model was also capable of simultaneously detecting additional species present within the mixed breeding colony, including erect-crested penguins, fulmar prions, and New Zealand fur seals, adding scalable efficiency gains for future surveys. Our results demonstrate that combining piloted aircraft surveys with AI-based image analysis provides a rapid, scalable, and accurate method for monitoring seabird populations, with substantial benefits for conservation management in remote and logistically constrained regions.