River plastic hotspot detection from space
Ámbar Pérez-García, Graciela Amanda, José F. López, Marc Rußwurm, Tim H.M. van Emmerik

TL;DR
This paper introduces a satellite-based method to detect plastic pollution hotspots in rivers using machine learning and cloud computing.
Contribution
A novel cloud-based pipeline using satellite data and machine learning to detect river plastic hotspots with high accuracy.
Findings
The method achieves up to 99.5% accuracy within rivers and 79% F1-score across rivers.
The approach is tested in three diverse river systems with promising results.
An open-access application is released for global plastic monitoring.
Abstract
Plastic pollution threatens terrestrial and aquatic ecosystems, and rivers play a central role in transporting and retaining plastics across landscapes. Effective mitigation requires scalable methods to identify riverine plastic accumulation hotspots. Here, we present a semi-automated, cloud-based pipeline that integrates satellite remote sensing and machine learning to detect river plastic hotspots. High-resolution PlanetScope imagery is used to annotate training regions, which are transferred to Sentinel-2 multispectral data to train Random Forest classifiers within Google Earth Engine. The approach is evaluated across three contrasting river systems—the Citarum (Indonesia), Motagua (Guatemala), and Odaw (Ghana)—to assess transferability under diverse environmental conditions. Intra-river transfer achieves up to 99.5% accuracy, while optimized inter-river transfer yields a plastic…
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Taxonomy
TopicsMicroplastics and Plastic Pollution · Toxic Organic Pollutants Impact · Effects and risks of endocrine disrupting chemicals
