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Detecting Floating Marine Macro Litter (FMML) Using Deep Learning Models
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A framework for detecting and tracking floating plastic debris in inland waters was developed using optical satellite image time series and spectral unmixing for subpixel-scale coverage estimation. Deployed as a Google Earth Engine app, the system requires only a time frame and area of interest to identify highly affected areas across multiple continents.
Floating plastic debris on water surfaces poses both immediate and long-term threats to the environment. Therefore, identifying and monitoring plastic pollution is crucial for understanding its location and scale. This article presents a framework for detecting and tracking floating plastic debris in inland waters using optical satellite image time series, leveraging the advantages of multitemporal Earth observation data. The detection process begins with a rule-based approach that analyzes variations in signal intensity, temporal patterns, spectral characteristics, and information fusion to identify potential plastic candidates. Once sensitive areas are detected, they can be continuously monitored, and the extent of plastic coverage at a subpixel level is estimated using spectral unmixing. The method requires only a specified time frame and area of interest as input parameters, eliminating the need for manually selecting specific images or outlining regions of interest. Several case studies demonstrate the successful application of this workflow, developed as a Google Earth Engine application, to identify highly affected areas in full Sentinel-2 scenes. These examples span different continents and environmental contexts, capturing floating plastic debris of varying types and dynamics.
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Towards Detecting Floating Objects on a Global Scale with Learned Spatial Features Using Sentinel 2
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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.
Automatic Detection and Identification of Floating Marine Debris Using Multispectral Satellite Imagery
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Researchers developed a machine learning approach using Sentinel-2 satellite imagery and extreme gradient boosting to automatically detect and distinguish floating plastic debris from other marine materials like driftwood and seaweed.
Modern Floating Marine Macrolitter Monitoring Approaches and Methods and Integration of Machine Learning Techniques
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This review examined modern methods and approaches for detecting floating marine macrolitter, including visual observations, trawling, radar remote sensing, and deep learning techniques. The authors analyzed recent advances in artificial neural networks and machine learning that have significantly improved the accuracy of identifying and classifying marine litter at the sea surface, with key challenges including object diversity and partial submersion.
Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation data
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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.
Satellite monitoring of terrestrial plastic waste
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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.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.