Abstract
A global beach litter assessment is challenged by use of low-efficiency methodologies and incomparable protocols that impede data integration and acquisition at a national scale. The implementation of an objective, reproducible and efficient approach is therefore required. Here we show the application of a remote sensing based methodology using a test beach located on the Saudi Arabian Red Sea coastline. Litter was recorded via image acquisition from an Unmanned Aerial Vehicle, while an automatic processing of the high volume of imagery was developed through machine learning, employed for debris detection and classification in three categories. Application of the method resulted in an almost 40 times faster beach coverage when compared to a standard visual-census approach. While the machine learning tool faced some challenges in correctly detecting objects of interest, first classification results are promising and motivate efforts to further develop the technique and implement it at much larger scales.
•Beach surveys with Unmanned Aerial Vehicles are 39 times faster than visual census.•Debris density underestimation due to low image resolution can be corrected.•Machine learning approach overestimates debris density due to false positives.•Litter classification by aerial images manual and automatic processing is reliable.•Methodological improvements are possible and results justify their implementation.