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Papers
61,005 resultsShowing papers similar to Enhancing the Detection of Coastal Marine Debris in Very High-Resolution Satellite Imagery via Unsupervised Domain Adaptation
ClearCoastal Marine Debris Detection and Density Mapping With Very High Resolution Satellite Imagery
Researchers used high-resolution satellite imagery combined with machine learning to detect and map coastal marine debris density in southern Japan, finding that satellite-based methods can estimate debris amounts and types on beaches with reasonable accuracy.
A Combination of Machine Learning Algorithms for Marine Plastic Litter Detection Exploiting Hyperspectral PRISMA Data
Researchers applied a combination of machine learning algorithms to hyperspectral satellite imagery from the PRISMA satellite to detect marine plastic litter along coastlines and ocean surfaces. The multi-algorithm approach improved detection accuracy over single-model methods and demonstrated the potential for satellite-based monitoring of ocean plastic pollution at scale.
Large-scale detection of marine debris in coastal areas with Sentinel-2
Researchers built a deep learning model to detect floating marine debris in coastal areas using satellite imagery from the Sentinel-2 program. The system achieved strong detection accuracy across multiple test sites and can monitor large stretches of coastline regularly. The tool could help environmental agencies track and respond to marine plastic pollution at a scale that manual surveys cannot match.
Towards Detecting Floating Objects on a Global Scale with Learned Spatial Features Using Sentinel 2
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.
Marine Debris Detection in Satellite Surveillance Using Attention Mechanisms
Researchers developed an approach combining satellite imagery with attention-based deep learning models to detect marine debris from space. The study found that a model using combined spatial and channel attention (CBAM) achieved the best performance, with a 77% detection score, outperforming other approaches tested. These findings suggest that AI-enhanced satellite surveillance could become a practical tool for monitoring ocean plastic pollution at scale.
Enhanced spatiotemporal mapping of urban wetland microplastics: An interpretable CNN-GRU approach using satellite imagery and limited samples
Researchers built an interpretable CNN-GRU deep learning model combining satellite remote sensing with limited in-situ measurements to map microplastic distribution in urban wetlands with enhanced spatiotemporal resolution, enabling more comprehensive monitoring with less field sampling.
Hybrid Deep Learning Approach for Marine Debris Detection in Satellite Imagery Using UNet with ResNext50 Backbone
Despite its title referencing marine debris detection, this paper develops a deep learning computer vision model for identifying marine debris in satellite imagery using a UNet architecture with a ResNext50 backbone — not a study of microplastic pollution itself. It is a remote sensing and machine learning engineering paper, and while the technology could support large-scale ocean plastic monitoring, the paper does not directly examine microplastics or their health effects.
Advanced Classification of Marine Pollutants Using Sentinel-2 Multispectral Thermal Imaging and Vision Transformer for Enhanced Water Quality Assessment
This study used satellite multispectral imaging from the Sentinel-2 platform combined with a Vision Transformer machine learning model to automatically classify different types of marine pollutants — including plastics, algae, and oil — from aerial imagery. The AI-based approach significantly outperformed traditional classification methods and could detect plastic debris patches across large ocean areas. Automated large-scale detection of marine plastic pollution from satellites could transform the way we monitor and respond to ocean plastic contamination.
Automatic Detection and Identification of Floating Marine Debris Using Multispectral Satellite Imagery
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.
Estimating microplastic concentrations in surface water using satellite-based turbidity measurements: a case study on the New River, VA
Researchers used satellite-derived turbidity measurements as a proxy for microplastic concentrations in the New River, Virginia, developing and validating a model that enables broader spatial and temporal monitoring of riverine microplastic pollution without intensive field sampling.
Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation Data
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.
Application of Remote Sensing for the Detection and Monitoring of Microplastics in the Coastal Zone of the Colombian Caribbean
Researchers explored using remote sensing technology, including Sentinel-2 satellite imagery and machine learning algorithms, to detect and monitor microplastic pollution along the Colombian Caribbean coast. The study found that combining multispectral satellite data with computational models shows promise for systematic, large-scale monitoring of coastal microplastic contamination in regions where ground-level surveillance remains limited.
Finding Plastic Patches in Coastal Waters using Optical Satellite Data
Researchers demonstrated for the first time that floating macroplastic patches can be detected in optical data from the European Space Agency's Sentinel-2 satellites, validating detections against ground-truth observations and identifying characteristics that distinguish plastic from other floating material.
Advancing floating macroplastic detection from space using hyperspectral imagery
Researchers evaluated the use of hyperspectral satellite and airborne imagery to detect floating plastic debris in rivers and oceans, addressing major challenges related to plastic spectral properties in field conditions. Remote sensing tools for plastic detection are important for large-scale monitoring of the macro-scale plastic that eventually becomes microplastics.
Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation data
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.
Abundance of Plastic-Litter in Hyperspectral Imagery Using Spectral Unmixing in Coastal Environment
This study tested whether hyperspectral satellite or aerial imagery combined with spectral unmixing algorithms can detect and map microplastic litter in coastal environments. Results showed the approach can identify plastic fragments smaller than a pixel by analyzing mixed spectral signals, offering a scalable monitoring tool. Remote sensing methods like this could greatly reduce the cost and labor of tracking coastal plastic pollution at large spatial scales.
An inversion model of microplastics abundance based on satellite remote sensing: a case study in the Bohai Sea
Researchers developed a satellite-based model to estimate microplastic concentrations in China's Bohai Sea using remote sensing data. The model combined water color measurements from satellites with field sampling to predict microplastic distribution across a large area. The study suggests that remote sensing could become a practical tool for monitoring ocean microplastic pollution over wide regions without relying solely on labor-intensive field sampling.
Enhancing marine debris identification with convolutional neural networks
A deep learning model was developed to identify and classify marine debris components captured by underwater remotely operated vehicle imagery, addressing the challenge of widely distributed ocean waste including microplastics. The convolutional neural network demonstrated improved accuracy for debris detection and classification compared to conventional image analysis methods.
MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data
Researchers created MARIDA, the first benchmark dataset using Sentinel-2 satellite imagery for machine learning-based marine debris detection, distinguishing plastic debris from co-existing features like algae, ships, and various water types across global locations.
Development of Novel Classification Algorithms for Detection of Floating Plastic Debris in Coastal Waterbodies Using Multispectral Sentinel-2 Remote Sensing Imagery
Researchers developed classification algorithms using Sentinel-2 satellite imagery to detect floating plastic debris in coastal waters near Cyprus and Greece. They tested both unsupervised and supervised methods and found that a semi-supervised fuzzy c-means approach achieved the highest accuracy for identifying plastics. The study demonstrates that remote sensing technology can be an effective tool for monitoring and mapping marine plastic pollution at scale.
Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery
Researchers tested experimental hyperspectral airborne imagery to detect floating macroplastics in rivers and the ocean, demonstrating that combining spectral and spatial features improves detection accuracy over single-band approaches.
Detection of Trash in Sea Using Deep Learning
Researchers developed a deep learning convolutional neural network (CNN) model to detect and classify trash in marine and aquatic environments from underwater images, aiming to overcome the limitations of manual debris detection for objects that may be submerged or partially obscured.
An Image Analysis of Coastal Debris Detection -Detection of microplastics using deep learning-
Researchers developed a deep learning-based coastal debris detection system using YOLOv7 and the SAHI vision library to identify microplastics in image data collected from shorelines. The system demonstrated effective detection performance and offers a scalable approach for automated monitoring of microplastic litter in coastal environments.
Proof of concept for a new sensor to monitor marine litter from space
Researchers analyzed 300,000 satellite images of the Mediterranean Sea to track floating marine litter over time, finding that heavy rainfall events drive most litter inputs from land while coastal currents and wind determine how it spreads. The study demonstrates that satellites can reliably map pollution hotspots and detect seasonal trends, making space-based monitoring a practical new tool for managing ocean plastic pollution.