Drone Insights: Unveiling Beach Usage through AI-Powered People Counting

• Published in: Drones (Basel) 2024-10, Vol.8 (10), p.579
• Main Authors: Herrera, César, Connolly, Rod M., Rasmussen, Jasmine A., McNamara, Gerrard, Murray, Thomas P., Lopez-Marcano, Sebastian, Moore, Matthew, Campbell, Max D., Alvarez, Fernando
• Format: Article
• Language: English
• Subjects: Accuracy; artificial intelligence; Automation; Aviation; Beaches; coastal management; Coasts; Computer vision; Counting; Datasets; Deep learning; Environmental conditions; Human error; infrastructure usage; Lifeguards; ocean beaches; Public safety; Surfing; Time of use; Tourism; Use statistics
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones8100579

Ocean beaches are a major recreational attraction in many coastal cities, requiring accurate visitor counts for infrastructure planning and value estimation. We developed a novel method to assess beach usage on the Gold Coast, Australia, using 507 drone surveys across 24 beaches. The surveys covered 30 km of coastline, accounting for different seasons, times of day, and environmental conditions. Two AI models were employed: one for counting people on land and in water (91–95% accuracy), and another for identifying usage types (85–92% accuracy). Using drone data, we estimated annual beach usage at 34 million people in 2022/23, with 55% on land and 45% in water—approximately double the most recent estimate from lifeguard counts, which are spatially limited and prone to human error. When applying similar restrictions as lifeguard surveys, drone data estimated 15 million visits, aligning closely with lifeguard counts (within 9%). Temporal (time of day, day of the week, season) and spatial (beach location) factors were the strongest predictors of beach usage, with additional patterns explained by weather variables. Our method, combining drones with AI, enhances the coverage, accuracy, and granularity of beach monitoring, offering a scalable, cost-effective solution for long-term usage assessment.