We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation Data
AI summary Read the abstract
Researchers developed a machine learning classifier using free Copernicus satellite data to detect plastic waste — including greenhouses, tyres, and waste sites — in both aquatic and terrestrial environments, achieving high accuracy and enabling low-cost large-scale plastic pollution mapping.
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.
More Papers Like This
Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation data
AI summary Read the abstract
Researchers used free Copernicus Earth observation satellite data and machine learning to detect waste plastic in marine and terrestrial environments at a large scale. The classifier was trained on Sentinel-1 and Sentinel-2 data and performed well for detecting larger plastic accumulations. Satellite-based detection could enable continuous, wide-area monitoring of plastic pollution at a fraction of the cost of ground surveys.
Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation data
AI summary Read the abstract
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.
Towards Detecting Floating Objects on a Global Scale with Learned Spatial Features Using Sentinel 2
AI summary Read the abstract
Researchers developed a machine learning approach using Sentinel-2 satellite imagery to detect floating plastic debris and marine litter on a global scale, demonstrating that learned spatial features can improve detection of large aggregations of floating objects on water surfaces.
Satellite monitoring of terrestrial plastic waste
AI summary Read the abstract
Researchers developed a neural network system analyzing Sentinel-2 satellite imagery to detect terrestrial plastic waste aggregations, identifying 996 confirmed waste sites across Southeast Asia — far more than existing public databases — and found that 19% of sites sit within 200 meters of waterways, posing high ocean leakage risk.
A learning approach for garbage and debris identification
AI summary Read the abstract
This study developed machine learning approaches for detecting garbage and plastic debris in river and ocean environments from imagery. The authors present detection models and discuss the challenges of building training datasets for environmental plastic monitoring systems.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.