Sustainable AI Data Annotation

5,000+ waste images annotated weekly at 91% accuracy for AI-driven recycling automation. A European smart city consortium developing AI systems for automated waste recognition and sorting required scalable annotation support across CCTV and bin imagery datasets. Existing local annotation providers were costly and difficult to scale, while inconsistent labeling quality limited model accuracy across complex urban waste environments.

"Scalable waste annotation workflows improved recycling AI accuracy, reduced operational costs and accelerated smart city automation across urban sustainability initiatives. "

Industry: Energy & Utilities
Service: Generative AI, Workflow Automation

5,000+: Images annotated weekly
91%: Waste object detection accuracy
−60%: Cost vs local European providers

Challenge

The client needed to train AI models capable of distinguishing between plastic, bio, metal and mixed waste objects across cluttered urban environments. Existing manual labeling operations were expensive, slow and operationally difficult to scale for city-wide sustainability initiatives. Standard annotation models also failed to address the complexity of custom waste taxonomies required for real-world smart city deployments.

Approach

SBL Infotech deployed MMS — its Managed Media Services platform — to govern the complete annotation workflow from secure image intake through taxonomy management, segmentation and training-data delivery. Dedicated annotation teams executed bounding-box labeling and instance segmentation tailored specifically for waste-recognition AI systems. Custom classification frameworks were developed to support multiple waste categories while continuous correction loops and quality validation processes improved class-level precision across datasets. Structured delivery operations enabled weekly deployment of model-ready training data through transparent, auditable workflows.

Outcome

The client transformed fragmented pilot-stage annotation operations into a scalable AI data pipeline capable of supporting large-scale recycling automation initiatives. Improved annotation quality increased model accuracy and accelerated the development of real-time waste recognition systems for smart city environments. The engagement reduced operational costs by 60%, improved model accuracy by 23% and established a reusable sustainability-focused annotation framework ready to support future ESG and urban automation initiatives across expanding sensor and imaging networks.

IX Case studies

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