The Challenge
Gangasagar Mela hosts millions of pilgrims over a few days in extremely dense conditions. Maintaining sanitation at this scale through manual inspection is structurally limited.
Authorities required a system that could continuously monitor cleanliness across multiple zones, flag issues in real time, and route alerts to the right sanitation teams without manual oversight.
The Solution
A distributed AI-powered sanitation monitoring system using camera-based monitoring units, edge computing, and a centralized cloud dashboard.
Cameras placed at strategic high-traffic zones capture continuous visual data. Each unit runs lightweight AI models locally (edge computing) to classify cleanliness conditions in real time. When the AI detects a sanitation issue exceeding threshold, it triggers an alert to the central dashboard with location and severity.
System Architecture
- Camera units at distributed monitoring zones
- Edge devices running computer vision models locally
- Secure data transmission to cloud servers
- Central dashboard with real-time alert feed and zone-level analytics
- Mobile alert routing to sanitation field teams
Key Capabilities
- Real-time cleanliness detection via computer vision
- Automated alert routing to nearest field team
- Zone-wise monitoring and analytics
- Centralized dashboard for command-and-control
- Scalable to additional zones without architectural changes
Impact
- Real-time visibility into sanitation status across multiple zones
- Reduced average response time for field teams
- More efficient allocation of cleaning crews based on data
- Reduced reliance on manual inspection patrols
- Scalable framework applicable to other large public events
Deployments
Tech Stack
[Quote from govt authority or sanitation department]
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