CleanRoute AI
AI-Powered Smart Waste Management System
An intelligent waste management platform that detects waste conditions, prioritizes complaints, optimizes crew routes, verifies cleanup, and provides operational analytics — powered by computer vision, machine learning, and route optimization.
AI / ML Models
Six intelligent models working together across detection, prioritization, routing, forecasting, and continuous learning.
| Model | Purpose |
|---|---|
| OpenCV + MobileNetV2 | Waste type detection, bin fill-level estimation, image embeddings |
| Decision Tree + Naive Bayes | Context-aware priority (school/hospital proximity, repeat reports, weather) |
| Google OR-Tools | Vehicle routing, crew capacity, priority deadlines, OSRM road distances |
| Linear / Poisson Regression | Waste tonnage forecasting, area-wise complaint forecasting, holiday-aware predictions |
| Isolation Forest | Illegal-dumping hotspots, complaint spikes, area-level anomalies |
| Continuous Learning | Admin priority corrections, approved cleanup labels, citizen-confirmed waste types, weekly model retraining |
Main Features
Detection, prioritization, routing, and forecasting — in one smart-city platform.
Image-Based Detection
Upload or capture waste images; AI detects waste type and estimates fill level using OpenCV + MobileNetV2.
Priority Classification
ML classifies complaints as Urgent / High / Medium / Low using Decision Tree + Naive Bayes with context-aware signals.
Smart Route Optimization
Google OR-Tools groups nearby complaints into efficient routes with crew capacity, priority deadlines, and OSRM road distances.
Waste Forecasting
Linear / Poisson Regression forecasts waste tonnage and area-wise complaints with holiday-aware predictions.
Anomaly Detection
Isolation Forest surfaces illegal-dumping hotspots, complaint spikes, and area-level anomalies.
Cleanup Verification
Crew confirms cleanup with after-photo and location, closing the loop from complaint to resolution.
Real-Time Dashboard
Interactive charts, live statistics, workload distribution, and AI-powered forecasting and anomaly insights.
Interactive Maps
Leaflet maps with priority-colored markers and route visualization for field operations.
Continuous Learning
Admin corrections, approved labels, and citizen confirmations feed weekly model retraining.
AI Pipeline — Step by Step
Citizen submits waste complaint with photo and location
AI detects waste type and estimates fill level
ML classifies complaint as Urgent / High / Medium / Low
System assigns complaint to the most suitable crew
Route engine groups nearby complaints into an efficient route
Crew reaches location and collects the waste
Cleanup is confirmed with after-photo and location
Dashboard tracks operations; AI provides forecasting and anomaly insights
| Metric | Result |
|---|---|
| Fill Level Accuracy | 67.6% |
| Priority Accuracy | 64.7% |
| Waste Forecast RMSE | 2.26 tons |
Status: Live project. Actively improving accuracy with more training data and model iteration.
Technology Stack
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