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A Proposed Technology Solution for Preventing Marine Littering Based on Uavs and Iot Cloud-based Data Analytics
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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.
Pollution in the marine environment is a global problem with severe consequences on marine and terrestrial ecosystemsand to human health. It is estimated that every year, eight million tonnes of debris find their way into the worlds
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This paper proposes using unmanned vehicles guided by waste tracking data to collect plastic marine litter around tourist islands. Autonomous cleanup technology could help remove plastic debris before it breaks down into microplastics and enters the food chain.
An Estimation Method of Coastal Ocean Debris Using Aerial Drone
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This study developed a drone-based aerial estimation method for detecting and quantifying coastal ocean debris, including plastic litter. The approach used aerial imagery analysis to address limitations of traditional survey methods for large-scale marine litter assessment.
Leveraging Artificial Intelligence for Plastic Waste Management in Coastal Waters: A Drone-Based Approach for Identification and Hotspot Detection
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A machine learning model trained on drone footage achieved 89.4% accuracy in classifying plastic debris types in coastal waters and identifying hotspot areas for targeted cleanup operations. The AI-driven approach offers faster and more scalable monitoring than traditional methods, though detection of small microplastics and environmental variability remain challenges.
Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods
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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.
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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.