  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.
