Anthropogenic litter density and composition data acquired flying commercial drones on sandy beaches along the Saudi Arabian Red Sea.

Anthropogenic litter density and composition data were obtained by conducting aerial surveys on 44 beaches along the Saudi Arabian Coast of the Red Sea [1]. The aerial surveys were completed with commercial drones of the DJI Phantom suite flown at a 10 m altitude. The stills have a resolution of less than 0.5 cm pixels-1, hence, litter objects of few centimetres like bottle caps are easily detectable in the drone images. We here provide a subsample of the drone images acquired. To spare the time needed to visually count the litter objects in the thousands of drone images acquired, these were automatically screened using an object detection algorithm, specifically a Faster R-CNN, able to perform a binary classification in litter and non-litter and to categorize the objects in classes. The multi-class classification, however, is a challenging problem and, hence, it was conducted only on the 15 beaches that showed the highest performance after the binary classification. The performance of the algorithm was calculated by visually screening a subsample of images and it was used to correct the output of the Faster R-CNN. The described steps allowed to obtain an estimate of the litter density in 44 beaches and the litter composition in 15 beaches. By multiplying the relative abundance of each litter class and the median weight of objects belonging to each class, we obtained an estimate of the total mass of plastic beached on 15 beaches. Possible predictors of litter density and mass are the population and marine traffic densities at the site, the exposure of the beach to the prevailing wind and the wind speed, the fetch length and the presence of vegetation where litter could get trapped. Making such raw data (i.e. litter density and composition and their predictors) available can help building the base for a robust global estimate of anthropogenic litter in coastal environments and it is particularly important if data regards an understudied region like the Arabian Peninsula. Moreover, we share a subsample of the original drone images to allow usage from stakeholders.

Enabling a large-scale assessment of litter along Saudi Arabian red sea shores by combining drones and machine learning.

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.