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River Waste Detection Methods for Urban Canals in Southeast Asia—A Review of Present Techniques and Future Perspectives
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This review looks at ways to spot floating plastic trash in Southeast Asia's urban canals, from cameras and drones to AI detection tools, before it breaks down into microplastics that can reach our water and food. The takeaway: smart monitoring tech shows promise for catching plastic early, but it still needs more real-world testing before it can reliably guide cleanup efforts.
Floating plastic debris in urban canals is a management-relevant precursor to downstream microplastic pollution. This structured narrative review synthesizes conventional field surveys, camera-, UAV-, and satellite-based image analysis and AI-assisted image-based monitoring. Emphasis is placed on Southeast Asian engineered canals, where monsoon-driven flow, tides, gates, turbidity, glare, occlusion, and organic debris challenge continuous observation. Conventional surveys provide verifiable composition data but limited temporal coverage. Camera systems increase observation frequency, while deep learning can automate detection and tracking; however, reported performance depends strongly on the dataset, site, target size, and validation design. Published studies show substantial losses under cross-site transfer and condition-specific gains from preprocessing rather than a universal accuracy threshold. The synthesis therefore develops a decision-oriented framework linking camera calibration, conditional preprocessing, site-separated validation, uncertainty reporting, and hydrological data to operational triggers for cleanup or interception. Current evidence supports monitoring and pilot decision support, while broader autonomous operation requires further field validation. Priorities include transparent evidence reporting, shared Southeast Asian datasets, standardized metrics and environmental descriptors, cross-site testing, and life-cycle evaluation of deployment cost and maintenance.
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Emerging advanced technologies for monitoring and managing plastic pollution in aquatic ecosystems
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Plastic pollution in our oceans and rivers—including tiny microplastic particles—is a growing threat to wildlife and, ultimately, human health, since these pollutants can work their way into the food chain. This review paper looks at how new technology like drones, satellite imaging, and AI can help scientists track plastic waste more efficiently and predict where it's spreading, making cleanup efforts smarter and faster. While these tools show real promise, the researchers note that even better AI systems are still needed to fully understand how plastic moves through our environment.
Ocean and River Plastic Waste Detection Using Computer Vision
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Millions of tons of plastic pour into our oceans every year, much of it carried by rivers—but tracking where it comes from has always relied on slow, manual surveys. This study built an AI camera system that automatically spots and counts floating plastic waste from drone and shoreline footage with over 91% accuracy, offering a faster way to find pollution hotspots. Better tracking matters because plastic waste that lingers in waterways breaks down into microplastics, which end up in the fish and water we consume, making tools like this useful for guiding cleanup efforts before pollution reaches our food and water supply.
Multi-Scale Hierarchical Attention Ensemble Network for Fine-Grained Riverine Waste Segmentation Using UAV Multispectral Imagery
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Researchers used a drone with a special five-band camera to fly over a river in Indonesia and trained an AI system to automatically spot and map plastic waste floating in the water, even distinguishing it from natural debris like leaves and branches. This matters because rivers are a major pathway for plastic to reach oceans and break down into microplastics, which can end up in our food and water; better, faster detection tools like this could help communities target cleanup efforts before waste spreads further. The study is an early proof-of-concept from one river during one season, so more testing is needed before it can be used widely.
Uncovering Plastic Pollution: A Scoping Review of Urban Waterways, Technologies, and Interdisciplinary Approaches
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This scoping review of 110 peer-reviewed studies examined plastic pollution in Southeast Asian waterways, identifying four interconnected research areas including urban pollution pathways, monitoring innovations, community interventions, and interdisciplinary perspectives. The review highlights IoT sensors and geospatial technologies transforming plastic tracking while noting persistent gaps in longitudinal studies and policy translation across the Global South.
Advancing environmental sustainability through emerging AI-based monitoring and mitigation strategies for microplastic pollution in aquatic ecosystems
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This review explores how artificial intelligence technologies, including machine learning, computer vision, and remote sensing, can improve the detection, tracking, and removal of microplastic pollution in waterways. Researchers found that AI-based approaches offer significant advantages over traditional monitoring methods for identifying microplastic distribution patterns. The study highlights the potential of AI-driven robotic systems to support more efficient and scalable environmental cleanup efforts.
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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.