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

TL;DR

This paper presents a neural network-based method to identify illegal waste dumping sites using satellite imagery, enabling rapid, cost-effective mapping to support urban environmental management in the Global South.

Contribution

It introduces a machine learning approach that leverages satellite data to detect and monitor illegal dumping sites, addressing data scarcity and scalability issues.

Findings

High accuracy in identifying known dumping sites

Effective prediction of new potential dumping locations

Demonstrated scalability over large urban areas

Abstract

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.