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Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation data
Summary
This study developed a machine learning classifier using free Copernicus Sentinel-1 and Sentinel-2 satellite data to detect waste plastics in both marine and terrestrial environments. A training dataset was manually digitized covering land cover classes including greenhouses, general plastic waste, tires, and waste sites, and the classifier combined an artificial neural network with a post-processing decision tree. Validation across five locations demonstrated high accuracy, showing the potential for large-scale, low-cost plastic waste mapping using open satellite data.
The detection of waste plastics in the marine and terrestrial environment using satellite Earth Observation data offers the possibility of large-scale mapping, and reducing on-the-ground manual investigation. In addition, costs are kept to a minimum by utilizing free-to-access Copernicus data. A Machine Learning based classifier was developed to run on Sentinel-1 and -2 data. In support of the training and validation, a dataset was created with terrestrial and aquatic cases by manually digitizing varying landcover classes alongside plastics under the sub-categories of greenhouses, plastic, tyres and waste sites. The trained classifier, including an Artificial Neural Network and post-processing decision tree, was verified using five locations encompassing these different forms of plastic. Although exact matchups are challenging to digitize, the performance has generated high accuracy statistics, and the resulting land cover classifications have been used to map the occurrence of plastic waste in aquatic and terrestrial environments.