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Papers
61,005 resultsShowing papers similar to Applications of Deep Learning-Based Super-Resolution Networks for AMSR2 Arctic Sea Ice Images
ClearFrom Local to Global: Efficient Dual Attention Mechanism for Single Image Super-Resolution
Researchers developed a dual attention mechanism for deep learning neural networks to improve single image super-resolution. This type of image enhancement technology could have applications in improving the detection and classification of microplastic particles in environmental images.
Enhancing the Detection of Coastal Marine Debris in Very High-Resolution Satellite Imagery via Unsupervised Domain Adaptation
Researchers proposed a satellite-based marine debris detection model using unsupervised domain adaptation to overcome limitations of applying high-resolution trained models to lower-resolution imagery. The approach improves practical applicability for monitoring coastal debris distributions across diverse satellite data sources.
Enhanced Microplastic Aggregation Prediction via Deep Learning and Spectral Analysis of Marine Snow Composition
Researchers developed a deep learning framework called the Spectral-Enhanced Aggregation Prediction Network that integrates spectral analysis of marine snow to improve prediction of microplastic aggregation rates in the ocean, addressing limitations of current models that struggle with the complex interplay of biological, chemical, and physical factors.
Enhanced Microplastic Aggregation Prediction via Deep Learning and Spectral Analysis of Marine Snow Composition
Researchers developed a deep learning framework called the Spectral-Enhanced Aggregation Prediction Network that integrates spectral analysis of marine snow to improve prediction of microplastic aggregation rates in the ocean, addressing limitations of current models that struggle with the complex interplay of biological, chemical, and physical factors.
A novel filtering method for geodetically determined ocean surface currents using deep learning
Researchers used deep learning to improve the accuracy of ocean current maps derived from satellite measurements of sea level and gravity. Better ocean current mapping helps scientists track where microplastics travel and accumulate in the ocean once they enter from rivers and coastlines.
Deep Learning Based Approach to Classify Saline Particles in Sea Water
Researchers developed a deep learning classification approach to identify saline particles in seawater images, demonstrating high accuracy in distinguishing salt crystals from other particles, with potential application to automated water quality monitoring systems.
A Review of Underwater Image Enhancement and Restoration Techniques Based on Gan
This review examines underwater image enhancement and restoration technologies based on generative adversarial networks (GANs), assessing challenges arising from the underwater imaging environment and evaluating GAN-based methods for improving image quality in support of marine operations and seabed resource development.
Deep-Sea Debris Identification Using Deep Convolutional Neural Networks
Researchers developed a deep convolutional neural network classifier to identify and distinguish deep-sea debris from seafloor imagery, demonstrating that automated AI-based detection can support submersible clean-up operations targeting marine debris in deep-sea environments.
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.
Satellite-based sea ice thickness changes in the Laptev Sea from 2002 to 2017: comparison to mooring observations
This study analyzed a 15-year satellite radar altimetry record of sea ice thickness in the Laptev Sea, finding declining trends consistent with Arctic warming. This sea ice climate science paper is not related to microplastic research.
SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements
Researchers developed SNOWED, an automatically constructed dataset of satellite imagery with labeled water edges, enabling deep learning models to accurately detect and monitor shoreline changes for environmental monitoring applications.
SDRCNN: A Single-Scale Dense Residual Connected Convolutional Neural Network for Pansharpening
SDRCNN is a new single-scale lightweight convolutional neural network designed for pansharpening, fusing high-resolution panchromatic and low-resolution multispectral satellite images to produce high-resolution multispectral outputs. The architecture was designed to balance spatial and spectral quality while minimizing computational cost.
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.
Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage
Researchers developed a deep learning approach to classify mesoscale oceanic eddy signatures in sea surface temperature satellite images, overcoming the challenges posed by complex eddy structures and cloud coverage that corrupts large fractions of imagery.
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.
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.
Coastal 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.
Polarimetric Imaging via Deep Learning: A Review
This review examined how deep learning techniques are advancing polarimetric imaging across applications including remote sensing, biomedical diagnosis, and autonomous vehicles, highlighting key architectures and remaining challenges in the field.
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.
Flux to Flow: a Clearer View of Earth’s Water Cycle Via Neural Networks and Satellite Data
This dissertation developed neural network methods to enhance the spatial resolution of satellite measurements of Earth's water cycle, enabling finer-scale monitoring of hydrological processes such as precipitation, evaporation, and runoff across diverse environments.
Improving YOLOv11 for marine water quality monitoring and pollution source identification
Researchers improved the YOLOv11 computer vision model to better detect and identify marine pollution sources, including oil spills, debris, and turbid water, in complex underwater environments. The enhanced model achieved higher detection accuracy and faster processing speeds compared to the standard version. The study demonstrates that advanced AI-based monitoring tools can meaningfully improve our ability to track and respond to marine pollution in real time.
The supporting role of Artificial Intelligence and Machine/Deep Learning in monitoring the marine environment: a bibliometric analysis
This review examines the supporting role of artificial intelligence and machine learning in monitoring and managing plastic pollution, covering applications in remote sensing, image-based plastic detection, and predictive modeling of plastic fate. The authors identify deep learning for image classification and satellite-based detection as the most rapidly advancing AI applications in plastic pollution science.
Automatic Identification and Classification of Marine Microplastic Pollution Based on Deep Learning and Spectral Imaging Technology
Researchers developed an AI system combining deep learning with multispectral imaging to automatically identify and classify marine microplastics, using a feature-selection method called ReliefF to reduce noise in complex ocean samples. The approach achieved high accuracy and offers a scalable solution for large-scale ocean microplastic monitoring that outperforms traditional manual inspection.
On the use of deep learning for phase recovery
Researchers reviewed how deep learning — a type of artificial intelligence — can recover phase information from light, which is typically lost when cameras capture images, enabling sharper microscopy and better materials analysis. These advances improve the tools scientists use to study tiny particles, including microplastics, at very fine scales.