Beach litter assessments rely on time inefficient and high human cost protocols, mining the attainment of global beach litter estimates. Here we show the application of an emerging technique, the use of drones for acquisition of high-resolution beach images coupled with machine learning for their automatic processing, aimed at achieving the first national-scale beach litter survey completed by only one operator. The aerial survey had a time efficiency of 570 ± 40 m² min⁻¹ and the machine learning reached a mean (±SE) detection sensitivity of 59 ± 3% with high resolution images. The resulting mean (±SE) litter density on Saudi Arabian shores of the Red Sea is of 0.12 ± 0.02 litter items m⁻², distributed independently of the population density in the area around the sampling station. Instead, accumulation of litter depended on the exposure of the beach to the prevailing wind and litter composition differed between islands and the main shore, where recreational activities are the major source of anthropogenic debris.

沙滩垃圾评估依赖于时间效率低和人工成本高的协议，可挖掘全球沙滩垃圾评估的实现情况。在这里，我们展示了一种新兴技术的应用，即使用无人机获取高分辨率的沙滩图像并结合机器学习对其进行自动处理，旨在实现仅由一个操作员完成的首次全国规模的沙滩垃圾调查。航测的时间效率为570±40 m ²  min ^-1，在高分辨率图像下，机器学习的平均（±SE）检测灵敏度为59±3％。在红海的沙特阿拉伯海岸上，所得平均（±SE）凋落物密度为m ^-2的0.12±0.02凋落物分布与采样站周围区域的人口密度无关。取而代之的是，垃圾的积累取决于海滩在盛行的风中的暴露，而在岛屿和主要海岸之间，垃圾的组成有所不同，那里的娱乐活动是人为碎片的主要来源。