River plastic hotspot detection from space.

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 F1-score of 79%, outperforming previously reported results of 69%. By providing an open-access Google Earth Engine application, this work enables reproducible, large-scale monitoring of riverine plastic pollution and supports the development of global, satellite-based assessment strategies.