Anthropogenic litter density and composition data acquired flying commercial drones on sandy beaches along the Saudi Arabian Red Sea
KAUST DepartmentMarine Science Program
Biological and Environmental Sciences and Engineering (BESE) Division
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Red Sea Research Center (RSRC)
Permanent link to this recordhttp://hdl.handle.net/10754/668935
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AbstractAnthropogenic litter density and composition data were obtained by conducting aerial surveys on 44 beaches along the Saudi Arabian Coast of the Red Sea . 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.
CitationMartin, C., Zhang, Q., Zhai, D., Zhang, X., & Duarte, C. M. (2021). Anthropogenic litter density and composition data acquired flying commercial drones on sandy beaches along the Saudi Arabian Red Sea. Data in Brief, 107056. doi:10.1016/j.dib.2021.107056
SponsorsWe thank Julia Reisser, Elena Corona, Núria Marbá, Dorte Krause-Jensen and the staff from the Coastal and Marine Resources core lab at KAUST for help during fieldwork; We thank Matthew McCabe and Stephen Parkes for providing the DJI Phantom 3 Adv and the relative training. This work was supported and funded by King Abdullah University of Science and Technology (KAUST) through the baseline funding of C.M.D. and X. Z.
JournalData in Brief
Except where otherwise noted, this item's license is described as NOTICE: this is the author’s version of a work that was accepted for publication in Data in Brief. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Data in Brief, [, , (2021-04-20)] DOI: 10.1016/j.dib.2021.107056 . © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/