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Papers
61,005 resultsShowing papers similar to Design of an Urban Domestic Waste Landfill Based on Aerial Image Segmentation and Ecological Restoration Theory
ClearMapping Waste Piles in an Urban Environment Using Ground Surveys, Manual Digitization of Drone Imagery, and Object Based Image Classification Approach
This study used drone imagery and image classification to map illegal waste dumps in a densely populated Malawian neighborhood. Better waste monitoring tools like drone-based detection are important for identifying sites where plastic waste accumulates and fragments into microplastics.
Ecotoxicological impacts of landfill sites: Towards risk assessment, mitigation policies and the role of artificial intelligence
This review examines the health and environmental risks posed by landfill sites, which act as reservoirs for both legacy and emerging pollutants including microplastics. Unregulated waste disposal and leachate contamination are linked to diseases in nearby communities, and laboratory studies show toxic effects on organisms from bacteria to birds. The authors recommend improving landfill design, leachate treatment, and exploring artificial intelligence to better predict and manage these pollution risks.
Targeting Plastics: Machine Learning Applied to Litter Detection in Aerial Multispectral Images
Researchers applied machine learning to aerial multispectral images for automated detection of plastic litter in natural areas, demonstrating that combining spectral data with classification algorithms can effectively identify and monitor plastic waste pollution.
Object Detection of Macroplastic Waste Using Unmanned Aerial Vehicles in Urban Canal
Researchers developed and tested an unmanned aerial vehicle-based system for detecting macroplastic waste along riverbanks and beaches using object detection algorithms. The system achieved reliable detection performance and offers a scalable tool for large-area plastic litter surveys.
Application of AI-Enabled Computer Vision Technology for Segregation of Industrial Plastic Wastes
Researchers developed an AI-powered computer vision system to segregate mixed industrial plastic wastes by polymer type, addressing a key barrier to effective plastic recycling. The system achieved high classification accuracy across common plastic categories, demonstrating that machine vision can improve sorting efficiency and recycled plastic quality.
Assessment of Household Solid Waste Generation and Composition by Building Type in Da Nang, Vietnam
A study of household solid waste in Da Nang, Vietnam combined satellite imagery and field surveys to measure waste generation and composition by building type. The findings provide a practical method for cities to better understand and manage their waste streams.
Source Apportionment of Microplastics in Environment from Sanitary Landfill: A Case Study of Muangpak Municipality Landfill, Thailand
Researchers traced the sources of microplastic contamination in and around a municipal landfill in Thailand, finding that landfill decomposition is a significant contributor of microplastics to the surrounding environment. The study suggests that better waste management practices at landfills could help reduce microplastic pollution in nearby soil and water systems.
Mapping the plastic legacy: Geospatial predictions of a microplastic inventory in a complex estuarine system using machine learning
Researchers applied machine learning techniques to develop geospatial predictions of microplastic inventory in a complex estuarine system, overcoming the limitations of coarse ocean basin models by accounting for the intricate geomorphological and hydrodynamic conditions that govern sediment-associated microplastic distribution.
A Proposed Technology Solution for Preventing Marine Littering Based on Uavs and Iot Cloud-based Data Analytics
This paper proposes a technological solution using unmanned aerial vehicles and automated collection systems to prevent marine littering at coastal hotspots. The approach aims to intercept plastic waste before it enters the ocean and breaks down into microplastics.
Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R
Researchers developed a convolutional neural network-based algorithm to automatically detect and quantify floating marine macro-litter in aerial images, training it on 3,723 images and integrating it into a web application for practical monitoring use.
Real-Time Instance Segmentation for Detection of Underwater Litter as a Plastic Source
Researchers developed a real-time instance segmentation system using neural networks to detect underwater plastic litter on the seafloor, targeting the approximately 70% of marine litter that sinks and serves as a major source of ocean microplastics.
Enhancing Waste Management with a Deep Learning-based Automatic Garbage Classifier
This paper is not about microplastics; it presents a deep learning convolutional neural network system for automatically classifying garbage by material type to improve waste sorting efficiency and reduce the labor burden of manual waste management.
Microplastics in Landfill Bodies: Abundance, Spatial Distribution and Effect of Landfill Age
Researchers examined microplastic distribution in landfill refuse across different age sections, finding that older landfill areas contain higher microplastic abundances, demonstrating that plastic waste progressively fragments into microplastics during long-term burial.
Cluster Mapping of Waste Exposure Using DBSCAN Approach: Study of Spatial Patterns and Potential Distribution in Bantul Regency
Researchers used spatial clustering analysis to map waste accumulation hotspots in the Bantul Regency of Indonesia, where landfill capacity has been repeatedly exceeded. The study identified clustered patterns of waste buildup near markets and collection points, information that could help local governments target interventions to reduce long-term microplastic contamination risks from mismanaged waste.
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.
UAV Approach for Detecting Plastic Marine Debris on the Beach: A Case Study in the Po River Delta (Italy)
UAV imaging was used to detect and map anthropogenic marine debris on beaches in the Po River Delta, Italy, testing different image processing strategies and demonstrating that centimeter-scale spatial resolution UAV surveys can efficiently locate macroplastics before they degrade into harder-to-remove microplastics.
Environmental pitfalls and associated human health risks and ecological impacts from landfill leachate contaminants: Current evidence, recommended interventions and future directions.
This review examined the environmental and health risks from landfill leachate contaminants, including microplastics, heavy metals, and organic pollutants, and assessed current evidence on their pathways into groundwater and surface water, ecological impacts, and mitigation strategies.
Projector deep feature extraction-based garbage image classification model using underwater images
Researchers developed a deep learning model using projector-based feature extraction to classify underwater garbage images, achieving high accuracy in identifying marine plastic debris and other waste types for automated ocean pollution monitoring.
Data Augmentation Using Background Replacement for Automated Sorting of Littered Waste
This paper describes using data augmentation techniques to improve machine learning models for automated sorting of litter in outdoor environments. Better waste sorting technology could improve plastic recycling rates and reduce the amount of plastic that ends up fragmenting into microplastics.