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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Environmental Sources Marine & Wildlife Policy & Risk Remediation Sign in to save

Garbage Watch AI

The iJournal Student Journal of the Faculty of Information 2022 3 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Lorena Almaraz De La Garza, Shengkai Chen, Benjamin Kelly, Hamid Parsazadeh, Pengyu Zhou

Summary

Researchers developed a computer vision prototype called 'Garbage Watch AI' to detect floating plastic trash in freshwater environments using cameras and machine learning. The system aims to provide continuous, open-access data on plastic sources and pathways in rivers and streams. It is a technology development paper rather than an environmental monitoring study.

Study Type Environmental

Global plastic pollution in waterways is a serious environmental concern predicted to increase in the coming years with many groups currently invested in monitoring and removing plastics from aquatic ecosystems. Still, available data on the sources and pathways of larger floating plastics in freshwater environments is scarce. To bridge this gap, we present a computer vision model prototype to identify trash in primarily natural settings and develop a plan for a future implementation of the model. This would include an additional camera system to identify and collect data on passing trash on the surface of waterways. A system utilizing computer vision technology deployed at multiple sites seeks to inform existing monitoring and removal efforts while providing valuable open-access environmental data for the broader scientific community.

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