Enabling a large-scale assessment of litter along Saudi Arabian red sea shores by combining drones and machine learning.
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Machine Intelligence & kNowledge Engineering Lab
Marine Science Program
Red Sea Research Center (RSRC)
Embargo End Date2022-03-02
Permanent link to this recordhttp://hdl.handle.net/10754/667865
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AbstractBeach 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.
CitationMartin, C., Zhang, Q., Zhai, D., Zhang, X., & Duarte, C. M. (2021). Enabling a large-scale assessment of litter along Saudi Arabian red sea shores by combining drones and machine learning. Environmental Pollution, 277, 116730. doi:10.1016/j.envpol.2021.116730
SponsorsWe thank Julia Reisser, Elena Corona, Núria Marbá and Dorte Krause-Jensen for help during fieldwork; the staff from the Coastal and Marine Resources core lab at KAUST for support during sampling cruises. We thank Matthew McCabe and Stephen Parkes for providing the DJI Phantom 3 Adv and the relative training. We thank anonymous reviewers and editors for the useful comments during the revision process.
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.
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