Optimizing Waste Collection: Data Gathering for City Administration

How can AI improve waste collection efficiency? We helped the city administration build a high-quality dataset that boosted waste bin monitoring accuracy by 87%. This enabled automation, cost reduction, and faster response times for municipal services.

Industry:
Housing and Utilities
Data:
1000 images
Timeline:
Industry:
Housing and Utilities
Data:
1000 images
Timeline:

Task:

The city administration required a high-quality dataset to train a neural network capable of tracking waste bin fill levels and ensuring timely collection. To enhance the model’s accuracy, the dataset needed to include images of waste bins of various types and capacities, captured under different lighting and weather conditions—ranging from clear skies to rain and snow. The ultimate goal was to optimize waste collection vehicle logistics and reduce operational costs.

Our Solution:

We implemented a comprehensive data collection strategy using two key approaches:

  • 01

    Crowdsourcing:

    To cover a wide range of scenarios, we engaged a broad network of contributors who captured 1000 images of waste bins across different city areas.

    This approach enabled us to quickly gather diverse images reflecting required variations in lighting and weather conditions.

  • 02

    Rapid Response Data Collection Team:

    To fill in missing scenarios, we assembled a mobile team. These specialists followed predefined routes, capturing waste bin images in challenging conditions such as nighttime, heavy rainfall, and densely populated residential areas.

Results:

  • 87% improvement in model accuracy for waste bin monitoring, significantly increasing system efficiency.

  • Optimized waste collection logistics: garbage trucks now respond to bin fill levels in real time, reducing costs and improving city sanitation.

  • The city administration gained an automated waste management tool, accelerating municipal services’ response to overfilled bins and reducing citizen complaints.

Our Cases

Case studies highlight how our services have enhanced AI model training and improved business outcomes across various industries See more
  • Data Collection for Anti-Spoofing Tasks

    Within a month, more than 10% of the entire database with over 2,000 photographs was collected

  • Data Collection for Facial and Speech Recognition

    Through data collection, the client improved their biometric system for facial and voice recognition by 21%.

  • Content Moderation on the Video

    Content moderation on the video hosting platform enabled a 99% reduction in the influx of prohibited content

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Andrey,
Head of Sales

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