🚨 RockShield AI

AI-Powered Rockfall Prediction & Real-Time Alert System for Open-Pit Mines

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Table of Contents

• Overview
• Features
• Recent Updates
• Architecture
• Tech Stack
• Project Structure
• Quick Start
• Usage Guide
• Screenshots
• API Documentation
• Contributing
• License
• Team & Contributors
• FAQ

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Overview

RockShield AI is a comprehensive rockfall prediction and real-time alert system designed specifically for open-pit mining operations. The platform combines AI/ML models, IoT sensor integration, satellite imagery, and interactive dashboards to help mining operations predict rockfall hazards, analyze slope stability, and maximize worker safety.

Real-time risk assessment, interactive satellite mapping, detailed analytics, and automated alerts - all in one scalable, low-cost, life-saving solution.

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Features

🎯 Core Capabilities

• 🤖 AI-Powered Risk Prediction

  • Machine learning models (XGBoost, Random Forest) for rockfall probability assessment
  • Slope stability analysis with Factor of Safety (FoS) calculations
  • Real-time risk classification (High/Medium/Low)
  • Confidence scoring and probability metrics

• 🛰️ Interactive Satellite Map

  • Live satellite imagery using ESRI World Imagery
  • Terrain view toggle for topographical analysis
  • 9 coal mining regions across India with risk zone overlays
  • 285+ active mine locations tracked
  • Color-coded risk zones (red = high risk, green = medium risk)
  • Clickable zones/markers with detailed risk information
  • Interactive popups showing zone names, mine counts, and risk scores

• 📊 Risk Analytics Dashboard

  • Comprehensive analytics with 6 interactive charts
  • Risk distribution across Indian mining locations
  • Monthly incident trends and seasonal patterns
  • Risk trigger analysis (rainfall, earthquake, human activity, natural erosion)
  • Location-based filtering and statistics
  • Bar charts, pie charts, area charts, and line graphs

• 🔮 Real-Time Prediction System

  • Rockfall risk assessment with customizable parameters
  • Slope stability calculations with Factor of Safety (FoS)
  • Circular gauge visualizations for instant risk assessment
  • Color-coded results (red/orange/green)
  • Professional empty states and loading animations
  • Contextual safety recommendations

• 🌐 Modern Web Interface

  • Beautiful gradient backgrounds with glassmorphism effects
  • Smooth Framer Motion animations throughout
  • Responsive design for desktop, tablet, and mobile
  • Consistent design language across all pages
  • Intuitive navigation with quick action buttons
  • Professional typography using Poppins font family

📡 Alert & Communication

• Automated SMS/Email Alerts (Twilio/SendGrid integration ready)
• Risk threshold notifications
• Real-time alert triggers based on ML predictions

📊 Analytics & Insights

• Explainable AI with SHAP and LIME integration
• Historical trend analysis
• Risk pattern recognition
• Safety margin calculations

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Recent Updates

✨ Latest Features (November 2025)

🛰️ Satellite Map Feature

• Added interactive satellite map with live imagery
• Integrated 9 coal mining regions across India
• Implemented risk zone overlays with clickable polygons
• Added satellite/terrain view toggle
• Created GeoJSON data structure for 285+ mine locations
• Built "Insights to Safety" educational panel

📊 Risk Analytics Dashboard

• Comprehensive analytics page with 6 interactive charts
• Location-based filtering system
• Monthly trends and seasonal analysis
• Risk trigger breakdown (rainfall, earthquake, human activity, erosion)
• Statistics cards showing risk distribution
• Real-time data visualization

🎨 Prediction Page Interface Redesign

• Complete UI overhaul with glassmorphism effects
• Replaced helicopter icon with mountain emoji (⛰️)
• Circular gauge visualizations for results
• Improved form inputs with icons
• Enhanced loading states and animations
• Professional empty states

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Architecture

System Components

1. Frontend Layer (React + TypeScript)

   • Hero page with feature highlights
   • Interactive satellite map
   • Risk analytics dashboard
   • Prediction interface
   • Navigation and routing

2. Backend Layer (Python Flask)

   • REST API endpoints
   • ML model serving
   • Data processing pipeline
   • Analytics calculations

3. ML Models (XGBoost, Random Forest)

   • Rockfall risk classification
   • Slope stability analysis
   • Feature engineering
   • Model inference

4. Data Layer

   • GeoJSON risk zone data
   • Mining location database
   • Historical incident data
   • Sensor data integration (ready)

Data Flow

User Input → Frontend → REST API → ML Models → Predictions → Frontend Display
                                          ↓
                                   Analytics Engine
                                          ↓
                                   Risk Assessment
                                          ↓
                            Alert System (if threshold exceeded)

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Tech Stack

Frontend

• Framework: React 18 with TypeScript
• Build Tool: Vite
• Styling: TailwindCSS
• Animations: Framer Motion
• Maps: Leaflet.js + React-Leaflet
• Charts: Recharts
• Icons: Lucide React
• Routing: React Router DOM

Backend

• Framework: Flask (Python)
• ML Libraries: scikit-learn, XGBoost, pandas, numpy
• Model Persistence: joblib
• API: RESTful endpoints

DevOps & Tools

• Linting: ESLint
• CSS Processing: PostCSS
• Package Manager: npm

Integrations (Ready)

• SMS Alerts: Twilio
• Email Alerts: SendGrid
• Explainable AI: SHAP, LIME
• Satellite Data: ESRI World Imagery, ISRO Bhuvan (configurable)

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Project Structure

RockShield-AI/
├── src/                          # Frontend source code
│   ├── Components/              # React components
│   │   ├── Hero.tsx             # Homepage hero section
│   │   ├── SatelliteMap.tsx     # Interactive satellite map
│   │   ├── RiskAnalytics.tsx    # Analytics dashboard
│   │   ├── PredictionPage.tsx   # Risk prediction interface
│   │   ├── Navigation.tsx       # Navigation bar
│   │   ├── Footer.tsx           # Footer component
│   │   └── ...
│   ├── data/                    # Static data and GeoJSON
│   │   └── coalRiskZones.ts     # Mining regions data
│   ├── Types/                   # TypeScript type definitions
│   ├── App.tsx                  # Main app component
│   ├── main.tsx                 # Entry point with routing
│   └── index.css                # Global styles
│
├── ml-service/                  # Python ML backend
│   ├── app.py                   # Flask REST API
│   ├── models/                  # Trained ML models
│   ├── generate_models.py       # Model training script
│   ├── requirements.txt         # Python dependencies
│   └── templates/               # HTML templates (if needed)
│
├── models/terranox/             # Terranox ML integration
├── tests/                       # Test files
├── .config/                     # Configuration files
├── package.json                 # Node dependencies
├── tsconfig.json                # TypeScript configuration
├── vite.config.ts               # Vite configuration
├── tailwind.config.js           # Tailwind CSS config
├── .env.example                 # Environment variables template
├── README.md                    # This file
├── IMPLEMENTATION_SUMMARY.md    # Satellite map documentation
├── RISK_ANALYTICS_FEATURE.md    # Analytics documentation
├── PREDICTION_PAGE_UPDATE.md    # Prediction page documentation
└── RockShield-AI.png            # Project screenshot

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Quick Start

Prerequisites

• Node.js (v18 or higher)
• Python (v3.8 or higher)
• npm or yarn
• pip

Installation

1. Clone the Repository

git clone https://github.com/Adesh2204/RockShield-AI.git
cd RockShield-AI

2. Set Up Frontend

# Install dependencies
npm install

# Start development server
npm run dev

The frontend will run on http://localhost:5175/

3. Set Up ML Backend

# Navigate to ml-service directory
cd ml-service

# Install Python dependencies
pip install -r requirements.txt

# Start Flask server
python app.py

The backend API will run on http://localhost:5001/

4. Access the Application

• Homepage: http://localhost:5175/
• Satellite Map: http://localhost:5175/satellite-map
• Risk Analytics: http://localhost:5175/analytics
• Prediction: http://localhost:5175/predict

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Usage Guide

Navigating the Platform

Homepage

• View feature highlights and system overview
• Access all major features via action buttons
• "Launch Live Demo" → Go to prediction page
• "Risk Analytics" → View analytics dashboard
• "Satellite Map" → Explore interactive map

Satellite Map (/satellite-map)

1. View Risk Zones: Red polygons indicate high-risk areas, green for medium risk
2. Toggle Views: Switch between satellite and terrain imagery
3. Click Zones: Get detailed information about mining regions
4. Explore Locations: 9 regions across India with 285+ mine locations
5. Navigate: Use quick action buttons to access other features

Risk Analytics Dashboard (/analytics)

1. View Statistics: High risk %, medium risk %, regions monitored
2. Filter by Location: Dropdown to view specific region data
3. Analyze Charts:
   • Bar chart: Risk distribution by location
   • Pie chart: Overall risk breakdown
   • Area chart: Monthly incident trends
   • Line chart: Risk score progression
   • Horizontal bar: Risk trigger analysis
4. Hover for Details: Interactive tooltips on all charts

Prediction Page (/predict)

Rockfall Risk Assessment:

1. Fill in parameters:
   • Latitude and Longitude
   • Rainfall (mm)
   • Triggers (count)
   • Landslide size
   • Administrative division
2. Click "Predict Risk Level"
3. View results:
   • Risk percentage (circular gauge)
   • Classification (High/Medium/Low)
   • Probability and confidence metrics

Slope Stability Analysis:

1. Fill in technical parameters:
   • Cohesion
   • Friction angle
   • Unit weight
   • Slope height and angle
   • Water pressure
   • Reinforcement type
2. Click "Calculate Factor of Safety"
3. View results:
   • Factor of Safety (FoS) value
   • Stability status (Unstable/Marginal/Stable)
   • Safety margin assessment
   • Interpretation guide

API Endpoints

Rockfall Risk Prediction

POST /predict
Content-Type: application/json

{
  "latitude": 22.5726,
  "longitude": 88.3639,
  "rainfall": 150.5,
  "triggers": 3,
  "landslide_size": "medium",
  "administrative_division": "West Bengal"
}

Response:
{
  "risk_probability": 65.4,
  "risk_class": "MEDIUM",
  "confidence": 0.87
}

Slope Stability Calculation

POST /predict-fos
Content-Type: application/json

{
  "cohesion": 25.0,
  "friction_angle": 30.0,
  "unit_weight": 18.5,
  "slope_height": 15.0,
  "slope_angle": 45.0,
  "water_pressure": 50.0,
  "reinforcement": "anchors"
}

Response:
{
  "factor_of_safety": 1.45,
  "status": "MARGINAL",
  "safety_margin": "Acceptable with monitoring"
}

Analytics Data

GET /analytics

Response:
{
  "location_data": [...],
  "monthly_trends": [...],
  "risk_distribution": [...],
  "triggers": [...],
  "summary": {...}
}

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Screenshots

Interactive dashboard showing DEM-based mine map with AI-driven risk heatmap and real-time notifications.

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API Documentation

For detailed API documentation, see:

• IMPLEMENTATION_SUMMARY.md - Satellite map feature
• RISK_ANALYTICS_FEATURE.md - Analytics dashboard
• PREDICTION_PAGE_UPDATE.md - Prediction interface

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Contributing

Contributions are welcome! To contribute:

1. Fork the repository
2. Create a feature branch:

   git checkout -b feature/amazing-feature

3. Commit your changes:

   git commit -m "Add amazing feature"

4. Push to the branch:

   git push origin feature/amazing-feature

5. Open a Pull Request

Development Guidelines

• Follow TypeScript/React best practices
• Maintain consistent code style (ESLint)
• Write descriptive commit messages
• Test thoroughly before submitting
• Update documentation as needed

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License

This project is licensed under the MIT License.
See LICENSE file for details.

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Team & Contributors

Core Team

• Adesh - @Adesh2204 - Lead Developer

Special Thanks

To all contributors and supporters who made this project possible!

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FAQ

Q: What sensors are compatible with RockShield-AI?
A: Any device capable of sending data in JSON format. Mock scripts for Arduino/Raspberry Pi are provided for testing. Real sensor integration requires basic API connectivity.

Q: How do I deploy this in production?
A:

• Frontend: npm run build and serve the dist/ folder
• Backend: Use Gunicorn/WSGI with Flask, set up proper environment variables
• Database: Configure production database for sensor data
• Security: Implement authentication, HTTPS, rate limiting

Q: Can I customize the ML models?
A: Yes! Train new models using ml-service/generate_models.py or replace existing models in ml-service/models/. The system supports any scikit-learn compatible model.

Q: How accurate are the predictions?
A: The models are trained on synthetic data for demonstration. For production use, retrain with real-world data from your specific mining operations for best accuracy.

Q: Can I add more mining regions?
A: Yes! Edit src/data/coalRiskZones.ts to add new GeoJSON polygons and markers for additional regions.

Q: Is there a mobile app?
A: Currently web-only, but the responsive design works well on mobile browsers. A dedicated mobile app is planned for future releases.

Q: How do I integrate real IoT sensors?
A: Configure sensor devices to POST data to the Flask API endpoints. The .env.example file shows required environment variables. Contact the team for sensor integration guides.

Q: Can I use this for other types of mining?
A: Yes! The system is adaptable. You'll need to retrain models with relevant data and update the risk zone definitions for your specific mining type.

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Contact & Support

For questions, issues, or feature requests:

• GitHub Issues: RockShield-AI/issues
• Email: Contact via GitHub profiles

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Built with ❤️ for safer mining operations worldwide

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Last Updated: November 6, 2025