# Satellite Monitoring of Terrestrial Plastic Waste

arXiv: https://arxiv.org/abs/2204.01485 · HTML: https://mlsanity.com/paper/2204.01485/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 50 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 2 of 2)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [A Survey on Programmatic Weak Supervision](https://mlsanity.com/paper/2202.05433/) (2022)

## Built on by (most cited first, 4 of 4)

- [Galileo: Learning Global & Local Features of Many Remote Sensing Modalities](https://mlsanity.com/paper/2502.09356/) (2025)
- [Solid Waste Detection, Monitoring and Mapping in Remote Sensing Images: A Survey](https://mlsanity.com/paper/2402.09066/) (2024)
- [Closing Gaps in Emissions Monitoring with Climate TRACE](https://mlsanity.com/paper/2511.19277/) (2025)
- [A Deep Learning Pipeline for Solid Waste Detection in Remote Sensing Images](https://mlsanity.com/paper/2502.06607/) (2025)

## Abstract (arXiv)

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.