How GeoAI Improves Tourist Beach Environments: Micro-Scale UAV Detection and Spatial Analysis of Marine Debris

Junho Ser and Byungyun Yang ()
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Junho Ser: Geospatial Science Lab, Dongguk University—Seoul Campus, 30, Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
Byungyun Yang: Department of Geography Education, Dongguk University—Seoul Campus, 30, Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea

Land, 2025, vol. 14, issue 7, 1-14

Abstract: With coastal tourism depending on clean beaches and litter surveys remaining manual, sparse, and costly, this study coupled centimeter-resolution UAV imagery with a Grid R-CNN detector to automate debris mapping on five beaches of Wonsan Island, Korea. Thirty-one Phantom 4 flights (0.83 cm GSD) produced 31,841 orthoimages, while 11 debris classes from the AI Hub dataset trained the model. The network reached 74.9% mAP and 78%/84.7% precision–recall while processing 2.87 images s −1 on a single RTX 3060 Ti, enabling a 6 km shoreline to be surveyed in under one hour. Georeferenced detections aggregated to 25 m grids showed that 57% of high-density cells lay within 100 m of the beach entrances or landward edges, and 86% within 200 m. These micro-patterns, which are difficult to detect in meter-scale imagery, suggest that entrance-focused cleanup strategies could reduce annual maintenance costs by approximately one-fifth. This highlights the potential of centimeter-scale GeoAI in supporting sustainable beach management.

Keywords: GeoAI; marine debris; UAV imagery; object detection; coastal tourism; beach management (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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