Kapler is an AI-powered Orbital Intelligence Platform designed to monitor satellites, track space debris, predict collision risks, and generate autonomous avoidance recommendations.
The project addresses one of the fastest-growing challenges in modern space operations:
The increasing congestion of Earth's orbital environment.
As thousands of new satellites enter orbit and space debris continues to accumulate, the probability of orbital collisions rises dramatically.
Kapler provides:
- Real-time orbital visualization
- Satellite and debris tracking
- Collision prediction
- Conjunction analysis
- Orbital risk assessment
- Autonomous maneuver recommendations
- Space weather monitoring
- Orbital intelligence dashboards
The platform combines modern AI systems, orbital mechanics, data visualization, and predictive analytics into a unified operational command center.
Earth orbit is becoming increasingly crowded.
Current estimates indicate:
- Tens of thousands of tracked objects
- Hundreds of thousands of debris fragments
- Thousands of active satellites
- Constant conjunction events
A single collision can:
- Destroy operational satellites
- Disrupt communication networks
- Create massive debris clouds
- Trigger cascading orbital failures
- Cause multi-billion-dollar losses
Current monitoring systems often suffer from:
- Information overload
- Manual analysis workflows
- Limited predictive intelligence
- Poor visualization capabilities
- Lack of autonomous decision support
Organizations need a platform capable of:
- Monitoring orbital activity
- Predicting risks early
- Simulating future trajectories
- Supporting operational decision-making
Kapler acts as an AI-assisted Orbital Operations Center.
The platform continuously:
- Collects orbital data
- Processes satellite trajectories
- Tracks debris populations
- Detects potential conjunctions
- Calculates collision probabilities
- Simulates future orbital paths
- Generates risk assessments
- Produces maneuver recommendations
- Visualizes all activity in real time
Track active satellites in Earth orbit.
Features:
- Satellite search
- NORAD lookup
- Orbit visualization
- Live orbital propagation
- Telemetry dashboard
Monitor orbital debris populations.
Features:
- Debris catalog visualization
- Risk classification
- Orbital clustering
- Density analysis
- Debris tracking
Identify potential orbital conjunctions.
Features:
- Collision probability scoring
- Miss distance calculations
- Risk prioritization
- Automated alert generation
AI-powered decision support system.
Capabilities:
- Risk analysis
- Maneuver simulation
- Alternative trajectory generation
- Safety optimization
Built using CesiumJS.
Features:
- High-fidelity Earth rendering
- Satellite visualization
- Debris visualization
- Orbit paths
- Camera controls
- Threat overlays
Mission-control style interface.
Displays:
- Active satellites
- Debris objects
- Collision alerts
- Space weather conditions
- Orbital analytics
- AI reasoning stream
Uses machine learning and predictive analytics to:
- Forecast conjunction events
- Analyze orbital behavior
- Prioritize threats
- Generate recommendations
Monitor environmental conditions affecting satellites.
Tracks:
- Solar activity
- Geomagnetic disturbances
- Orbital environment conditions
┌─────────────────┐
│ Orbital Data │
│ Sources │
└────────┬────────┘
│
▼
┌───────────────────────┐
│ Data Processing Layer │
└───────────┬───────────┘
│
▼
┌───────────────────────┐
│ Collision Prediction │
│ Engine │
└───────────┬───────────┘
│
▼
┌───────────────────────┐
│ AI Decision Engine │
└───────────┬───────────┘
│
▼
┌───────────────────────┐
│ REST APIs │
└───────────┬───────────┘
│
▼
┌───────────────────────┐
│ React Frontend │
└───────────┬───────────┘
│
▼
┌───────────────────────┐
│ Cesium Visualization │
└───────────────────────┘
- React
- TypeScript
- Vite
- Tailwind CSS
- CesiumJS
- React Query
- Zustand
- Framer Motion
- Python
- FastAPI
- Uvicorn
- Pydantic
- AsyncIO
- MongoDB
- Motor
- MongoDB Atlas
- Scikit-Learn
- XGBoost
- TensorFlow
- PyTorch
- NumPy
- Pandas
- Orekit
- Skyfield
- Poliastro
- SGP4
- CesiumJS
- Recharts
- Three.js
- D3.js
- Docker
- GitHub Actions
- Nginx
orbital-guardian/
│
├── frontend/
│ ├── public/
│ ├── src/
│ │
│ ├── components/
│ ├── pages/
│ ├── hooks/
│ ├── store/
│ ├── services/
│ ├── utils/
│ ├── assets/
│ └── styles/
│
├── backend/
│ ├── app/
│ │
│ ├── api/
│ ├── services/
│ ├── ai/
│ ├── orbital/
│ ├── database/
│ ├── models/
│ ├── schemas/
│ ├── middleware/
│ └── utils/
│
├── docs/
│
├── datasets/
│
├── scripts/
│
├── docker/
│
├── tests/
│
├── .env
├── docker-compose.yml
├── requirements.txt
└── README.mdOrbital Sources
│
▼
Data Ingestion
│
▼
Trajectory Processing
│
▼
Collision Detection
│
▼
Risk Assessment
│
▼
AI Recommendation Engine
│
▼
REST API Layer
│
▼
Frontend Dashboard
│
▼
Cesium Earth Visualization
| Module | Purpose |
|---|---|
| Satellite Tracking | Monitor active satellites |
| Debris Monitoring | Track orbital debris |
| Collision Engine | Detect conjunction risks |
| Risk Scoring | Prioritize threats |
| AI Recommendations | Generate maneuver suggestions |
| Space Weather | Environmental monitoring |
| Visualization Engine | 3D orbital display |
| Analytics Dashboard | Operational intelligence |
git clone https://github.com/yourusername/orbital-guardian.git
cd orbital-guardiancd frontend
npm install
npm run devcd backend
pip install -r requirements.txt
uvicorn app.main:app --reloadLocal MongoDB:
mongodb://localhost:27017or
MongoDB Atlas:
MONGODB_URI=your_connection_stringKapler is designed for large-scale orbital monitoring.
Future capabilities:
- Millions of tracked objects
- Multi-region deployment
- Real-time streaming
- Distributed processing
- Global satellite monitoring
- NASA
- ISRO
- ESA
- JAXA
- Starlink
- OneWeb
- Planet Labs
- Aerospace research
- Orbital studies
- Space situational awareness
- Satellite analytics
- Orbital dynamics
- Space debris analysis
- Satellite tracking
- Debris visualization
- Collision monitoring
- Advanced conjunction analysis
- Predictive AI models
- Autonomous recommendations
- Digital twin of Earth's orbital environment
- Multi-agent mission planning
- Orbital traffic management
- Commercial deployment
- Global orbital intelligence network
Contributions are welcome.
Areas of interest:
- Aerospace Engineering
- AI & Machine Learning
- Orbital Mechanics
- Data Visualization
- Backend Development
- Frontend Development
This project is released under the MIT License.
Building the future of autonomous orbital safety through AI-powered space intelligence.
"Protecting Earth’s orbital environment, one trajectory at a time." 🚀🌍