We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
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.
Plastic waste is a significant environmental pollutant that is difficult to monitor. We created a system of neural networks to analyze spectral, spatial, and temporal components of Sentinel-2 satellite data to identify terrestrial aggregations of waste. The system works at wide geographic scale, finding waste sites in twelve countries across Southeast Asia. We evaluated performance in Indonesia and detected 374 waste aggregations, more than double the number of sites found in public databases. The same system deployed in Southeast Asia identifies 996 subsequently confirmed waste sites. For each detected site, we algorithmically monitor waste site footprints through time and cross-reference other datasets to generate physical and social metadata. 19% of detected waste sites are located within 200 m of a waterway. Numerous sites sit directly on riverbanks, with high risk of ocean leakage.
More Papers Like This
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.
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
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.
A method for detecting plastic waste floating using Sentinel 2 high spatial resolution image: a case study in the coastal area of Vietnam
AI summary Read the abstract
This study used Sentinel-2 high-resolution satellite imagery to detect and classify floating plastic waste in coastal waters of Vietnam. A spectral differentiation approach successfully identified plastic mesh in shallow coastal areas, demonstrating remote sensing as a viable tool for ocean plastic waste monitoring.
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.
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.