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Papers
20 resultsShowing papers similar to Developing Beach Litter Monitoring System Based on Reflectance Characteristics and its Abundance
ClearQuantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods
Researchers developed an object-oriented machine learning classification strategy using unmanned aerial system imagery to automatically identify and quantify marine macro litter on sandy beaches, comparing three automated methods against manual counts. The UAS-based approach demonstrated capacity for scalable, cost-effective beach litter monitoring to support coastal pollution surveillance programs.
Quantifying Marine Plastic Debris in a Beach Environment Using Spectral Analysis
Researchers analyzed shortwave infrared reflectance spectra of weathered marine plastic debris on sandy beaches, finding that polymer type significantly influences detection capability at sub-pixel surface covers relevant to remote sensing applications.
Towards the Spectral Mapping of Plastic Debris on Beaches
This paper reviews the use of remote sensing (satellite and aerial imaging) to detect and map plastic debris on beaches. Advances in spectral imaging could allow large-scale automated monitoring of coastal plastic pollution, which is currently labor-intensive and limited in coverage.
Development of Drifting Debris Detection System using Deep Learning on Coastal Cleanup
Researchers developed a deep learning-based system to detect litter on beaches using images and automated object recognition. Efficient litter detection tools could help coastal cleanup programs identify and remove plastic debris before it breaks down into microplastics.
Comparison of learning models to predict LDPE, PET, and ABS concentrations in beach sediment based on spectral reflectance
Researchers compared machine learning models to predict concentrations of LDPE, PET, and ABS microplastics in beach sediments using visible-near-infrared spectral reflectance, demonstrating that spectroscopic methods can efficiently estimate microplastic pollution in understudied terrestrial and coastal environments.
Application of hyperspectral imaging and machine learning for the automatic identification of microplastics on sandy beaches
Hyperspectral imaging combined with machine learning was applied to identify and classify microplastics on sandy beach surfaces, offering a faster and more scalable alternative to conventional spectroscopic analysis for large-area environmental monitoring.
Microplastic deposits prediction on Urban Sandy Beaches: Integrating Remote Sensing, GNSS Positioning, µ-Raman Spectroscopy, and Machine Learning Models
Researchers integrated remote sensing, GNSS altimetric surveys, micro-Raman spectroscopy, and machine learning models to predict microplastic deposition patterns on urban sandy beaches along the central Sao Paulo coastline, finding MP concentrations ranging from 6 to 35 MPs/m2.
Computer vision segmentation model—deep learning for categorizing microplastic debris
Researchers developed a deep learning computer vision model for automatically categorizing beached microplastic debris from images. The segmentation model was trained to identify and classify different types of microplastic particles, reducing the need for time-consuming manual counting and laboratory analysis. The study suggests that automated image-based detection could enable more scalable and consistent monitoring of microplastic pollution along coastlines.
Hyperspectral longwave infrared reflectance spectra of naturally dried algae, anthropogenic plastics, sands and shells
Researchers measured the light reflectance spectra of common beach litter materials — plastics, algae, sand, and shells — to develop reference data for identifying plastic pollution in remote sensing imagery. This spectral library is a key step toward using satellites and drones to detect and map plastic pollution on beaches and coastlines at large scale.
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.
Detection and assessment of marine litter in an uninhabited island, Arabian Gulf: A case study with conventional and machine learning approaches
Researchers surveyed marine litter on a remote Arabian Gulf island after a large cleanup, then trained a YOLO-v5 deep learning model on 10,400 beach images to automatically detect debris, achieving 90% detection accuracy and demonstrating that windward shores accumulate significantly more litter from neighboring countries.
Deep-Feature-Based Approach to Marine Debris Classification
This study applied deep learning to classify marine debris from images, demonstrating that feature-based neural network approaches can effectively distinguish plastic types and other debris categories to support automated ocean monitoring.
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.
Indoor spectroradiometric characterization of plastic litters commonly polluting the Mediterranean Sea: toward the application of multispectral imagery
Researchers used a laboratory spectrometer to measure the light reflectance of common plastic types found in the Mediterranean Sea as a step toward developing remote sensing methods to detect marine plastic pollution from satellites or aircraft. Aerial monitoring of plastic pollution could revolutionize our ability to track and manage large-scale ocean plastic contamination.
Microplastic Deposits Prediction on Urban Sandy Beaches: Integrating Remote Sensing, GNSS Positioning, µ-Raman Spectroscopy, and Machine Learning Models
Researchers used remote sensing, GNSS positioning, Raman spectroscopy, and machine learning to predict microplastic deposition on urban beaches along the Sao Paulo coastline in Brazil. Microplastic concentrations ranged from 6 to 35 particles per square meter, with the highest densities near the Port of Santos linked to industrial activities. The predominant types were foams, fragments, and pellets, and machine learning models showed high predictive accuracy for mapping their distribution.
Concept for a hyperspectral remote sensing algorithm for floating marine macro plastics
Researchers developed a reflectance model for how sunlight interacts with floating plastic debris on the ocean surface, accounting for plastic color, transparency, and shape, as a foundational step toward a hyperspectral remote sensing algorithm capable of detecting marine macroplastics from aircraft or satellite.
Litter segmentation with LOTS dataset
Not a microplastics paper — this computer science paper presents a machine learning benchmark for detecting and segmenting beach litter (including plastic debris) in sand using deep learning image segmentation models, contributing tools that could help automate coastal pollution monitoring.
Advancing Plastic Pollution Monitoring Through Enhanced Protocols and Deep Learning: applicability and effectiveness in real-world scenarios (Le Stang, France)
Researchers developed and tested a deep learning image analysis tool to enhance monitoring of beach plastic pollution, specifically targeting meso- and large microplastics at the wrack line in Brittany, France. The AI model achieved high detection accuracy under real-world conditions and integrated with established French national monitoring protocols, demonstrating feasibility for scalable automated beach litter surveillance.
Microplastic deposit predictions on sandy beaches by geotechnologies and machine learning models
Researchers used geotechnologies and machine learning models to predict microplastic deposition hotspots on sandy beaches, identifying environmental and anthropogenic variables that drive spatial variation in beach microplastic accumulation.
Use of UAVs and Deep Learning for Beach Litter Monitoring
Researchers developed an autonomous beach litter monitoring pipeline using UAV drone surveys combined with a YOLOv5 deep learning object detection algorithm trained on footage from Malta, Gozo, and the Red Sea coast. The system achieved a mean average precision (mAP50-95) of 0.252 across all litter classes and incorporated geolocation and digital elevation model data to support future autonomous retrieval robots.