Satellite Monitoring of Terrestrial Plastic Waste
This addresses the environmental issue of plastic pollution for monitoring agencies and policymakers by providing a scalable detection method, though it is incremental as it applies existing neural network techniques to new satellite data.
The researchers tackled the problem of monitoring terrestrial plastic waste by developing a neural network system that analyzes Sentinel-2 satellite data to identify waste aggregations at a continental scale, detecting 374 sites in Indonesia (more than double existing databases) and 996 confirmed sites across Southeast Asia.
Plastic waste is a significant environmental pollutant that is difficult to monitor. We created a system of neural networks to analyze spectral, spatial, and temporal components of Sentinel-2 satellite data to identify terrestrial aggregations of waste. The system works at continental scale. We evaluated performance in Indonesia and detected 374 waste aggregations, more than double the number of sites found in public databases. The same system deployed across twelve countries in Southeast Asia identifies 996 subsequently confirmed waste sites. For each detected site, we algorithmically monitor waste site footprints through time and cross-reference other datasets to generate physical and social metadata. 19% of detected waste sites are located within 200 m of a waterway. Numerous sites sit directly on riverbanks, with high risk of ocean leakage.