# Mapping illegal waste dumping sites with neural-network classification of satellite imagery

arXiv: https://arxiv.org/abs/2110.08599 · HTML: https://mlsanity.com/paper/2110.08599/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 16 Oct 2021.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells](https://mlsanity.com/paper/1802.07465/) (2018)

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

- [Solid Waste Detection, Monitoring and Mapping in Remote Sensing Images: A Survey](https://mlsanity.com/paper/2402.09066/) (2024)
- [AI-based Waste Mapping for Addressing Climate-Exacerbated Flood Risk](https://mlsanity.com/paper/2604.18151/) (2026)
- [The identification of garbage dumps in the rural areas of Cyprus through the application of deep learning to satellite imagery](https://mlsanity.com/paper/2308.02502/) (2023)
- [Open-access model for detecting openly dumped dispersed municipal solid waste from crowdsourced UAV imagery in Sub-Saharan Africa](https://mlsanity.com/paper/2605.02316/) (2026)

## Abstract (arXiv)

Public health and habitat quality are crucial goals of urban planning. In recent years, the severe social and environmental impact of illegal waste dumping sites has made them one of the most serious problems faced by cities in the Global South, in a context of scarce information available for decision making. To help identify the location of dumping sites and track their evolution over time we adopt a data-driven model from the machine learning domain, analyzing satellite images. This allows us to take advantage of the increasing availability of geo-spatial open-data, high-resolution satellite imagery, and open source tools to train machine learning algorithms with a small set of known waste dumping sites in Buenos Aires, and then predict the location of other sites over vast areas at high speed and low cost. This case study shows the results of a collaboration between Dymaxion Labs and Fundaci'on Bunge y Born to harness this technique in order to create a comprehensive map of potential locations of illegal waste dumping sites in the region.