Detecting and Preventing Illegal Dumping Worldwide Using AI

Background

Illegal dumping creates widespread environmental, economic, and health hazards. With over 55,000 reports of illegal dumping across 110 countries, addressing this issue requires innovative solutions. TrashOut, an initiative focused on mapping dumpsites and promoting recycling, partnered with Omdena to develop AI models capable of identifying and predicting illegal dumpsites. This project, completed in just two months, united over 40 collaborators globally to tackle this pressing challenge​.

Objective

To build machine learning models that:

1. Identify spatial patterns of illegal dumpsites.
2. Predict potential future dumpsites.
3. Analyze the causes and preventative measures for illegal dumping.

This initiative aimed to empower communities and authorities with actionable insights to curb illegal dumping and promote better waste management​.

Approach

1. Identifying Spatial Patterns

Analyzed dumpsites in six representative cities worldwide:

• Bratislava (Slovakia, Europe)
• Campbell River (Canada, North America)
• London (UK, Europe)
• Mamuju (Indonesia, Asia)
• Maputo (Mozambique, Africa)
• Torreón (Mexico, Central America)

2. Predicting Potential Dumpsites

Developed a machine learning model using TrashOut’s dataset, combined with socio-economic and waste production data. Key influencing variables were identified to predict the probability of a location becoming a dumpsite.

3. Global Analysis

Utilized additional datasets like What a Waste and World Bank Indicators to perform a global cluster analysis, categorizing countries based on waste production and socio-economic factors​

Results and Impact

• AI Heatmaps: Visualized high-risk zones for illegal dumping in urban and rural areas.
• Global Insights: Clustering analysis revealed patterns tied to socio-economic conditions.
• Actionable Solutions: Local authorities and communities gained tools for targeted interventions.

The project showcased AI’s ability to address global waste challenges, achieving 80% model accuracy and setting a foundation for scalable solutions​.

Future Implications

This initiative demonstrates the potential of AI in shaping waste management and environmental policies. By integrating predictive models with public participation and governmental action, illegal dumping can be significantly reduced. Further refinements in model features and expanded datasets could lead to more comprehensive and globally applicable solutions.

Find all the technical details of the project here!