Martin Cecilia, Zhang Qiannan, Zhai Dongjun, Zhang Xiangliang, Duarte Carlos M
Red Sea Research Center and Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Data Brief. 2021 Apr 20;36:107056. doi: 10.1016/j.dib.2021.107056. eCollection 2021 Jun.
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, 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.
通过对红海沙特阿拉伯海岸沿线的44个海滩进行空中调查，获取了人为垃圾密度和成分数据[1]。空中调查使用大疆精灵系列的商用无人机在10米高度飞行完成。所拍摄的静止图像像素分辨率小于0.5厘米，因此，像瓶盖这样几厘米大小的垃圾物体在无人机图像中很容易被检测到。我们在此提供所获取的无人机图像的一个子样本。为了节省在数千张所获取的无人机图像中人工清点垃圾物体所需的时间，使用一种目标检测算法，具体来说是一种更快的区域卷积神经网络（Faster R-CNN）对这些图像进行自动筛选，该算法能够对垃圾和非垃圾进行二元分类，并将物体分类到不同类别。然而，多类别分类是一个具有挑战性的问题，因此，仅对在二元分类后表现最佳的15个海滩进行了多类别分类。通过目视筛选图像子样本计算该算法的性能，并用于校正更快的区域卷积神经网络（Faster R-CNN）的输出。上述步骤使得能够获得44个海滩的垃圾密度估计值以及15个海滩的垃圾成分。通过将每个垃圾类别的相对丰度与属于每个类别的物体的中位数重量相乘，我们得到了15个海滩上搁浅塑料总质量的估计值。垃圾密度和质量的可能预测因素包括该地点的人口和海上交通密度、海滩对盛行风的暴露程度和风速、风区长度以及可能截留垃圾的植被的存在情况。提供这样的原始数据（即垃圾密度和成分及其预测因素）有助于为沿海环境中人为垃圾的可靠全球估计奠定基础，并且如果数据涉及像阿拉伯半岛这样研究较少的地区，这一点尤为重要。此外，我们分享原始无人机图像的一个子样本，以供利益相关者使用。
