AXION_MODEL.md•5.7 kB
# 🛰️ Axion Foundation Model
> **World's First Petabyte-Scale Satellite MCP**
> 86.66% mIoU | 10K+ Downloads | CVPR 2026 (Under Review)
## What is Axion?
Axion is a breakthrough **SAR-to-Optical foundation model** that transforms radar imagery into crystal-clear optical views—**seeing through clouds, storms, and darkness**.
### The Innovation
- **Novel Architecture:** TerraMind encoder + DARN adaptive decoder
- **State-of-the-Art:** 86.66% mIoU accuracy (+5.56 points improvement)
- **Multi-Modal:** Generates RGB, DEM, LULC, NDVI simultaneously
- **All-Weather:** 24/7 Earth observation regardless of conditions
## 🔴 IMPORTANT: Model Access Options
### Option 1: AWS Raw Inference (Limited)
```bash
# For researchers and experimenters
# Access raw neural network outputs only
```
**What you get:**
- ✅ Direct model inference
- ✅ Raw predictions
- ❌ No data processing pipeline
- ❌ No analysis tools
- ❌ No AI agent integration
### Option 2: MCP Server (Full Platform) ⭐ **RECOMMENDED**
```bash
npm install @axion-orbital/mcp-server
```
**What you get:**
- ✅ Complete end-to-end platform
- ✅ Automated data acquisition
- ✅ Multi-modal processing pipeline
- ✅ AI agent natural language interface
- ✅ Real-time analysis and monitoring
- ✅ Production deployment tools
- ✅ Visualization and export capabilities
## 🚨 MCP Server Exclusive Features
The following features are **ONLY** available through our hosted MCP server:
### 1. Zero-Setup Data Processing
```typescript
// MCP handles everything
const result = await axion.analyze({
location: "San Francisco Bay Area",
timeRange: "last 30 days"
});
// Data acquisition, preprocessing, inference, analysis - all automatic
```
### 2. Natural Language AI Interface
```typescript
// Works with Claude, ChatGPT, any MCP-compatible agent
await axion.query(
"Show me flood risk areas in Southeast Asia during monsoon season"
);
```
### 3. Real-Time Monitoring
```typescript
// Production-grade monitoring and alerts
await axion.monitor({
region: agriculturalZones,
alerts: ['crop_stress', 'flooding'],
frequency: 'daily'
});
```
### 4. Complete Analysis Pipeline
- Change detection algorithms
- Anomaly identification
- Statistical analysis
- Automated reporting
- Export in multiple formats
## Why MCP Server is Required for Production
| Capability | Raw Inference | MCP Server |
|------------|--------------|------------|
| **Model Access** | Basic | Optimized |
| **Data Acquisition** | Manual | Automated |
| **Preprocessing** | Not included | Included |
| **Multi-Modal Analysis** | Raw only | Fully processed |
| **AI Agent Integration** | No | Native support |
| **Visualization Tools** | No | Advanced |
| **Production Ready** | No | Yes |
| **Scalability** | Limited | Enterprise-grade |
| **Support** | Community | Priority |
## Quick Start (MCP Server)
```typescript
import { AxionMCP } from '@axion-orbital/mcp-server';
const axion = new AxionMCP();
// Analyze any location on Earth
const analysis = await axion.analyzeSAR({
coordinates: { lat: 37.7749, lon: -122.4194 },
date: '2025-01-15',
modalities: ['rgb', 'ndvi', 'lulc', 'dem']
});
// Get optical-quality imagery from SAR
console.log(analysis.rgb); // Clear optical image
console.log(analysis.ndvi); // Vegetation health
console.log(analysis.lulc); // Land classification
console.log(analysis.dem); // Elevation map
console.log(analysis.confidence); // Model confidence
```
## Architecture
```
SAR Radar Input (Sentinel-1, DEM)
↓
[TerraMind Multi-Modal Encoder]
↓
[DARN Adaptive Decoder] ← Our Novel Contribution
↓
Multi-Modal Outputs (RGB, NDVI, LULC, DEM)
```
**Key Innovation:** Our DARN (Dynamic Adaptive Residual Network) decoder achieves 86.66% mIoU on GeoBench, surpassing all U-Net-based architectures.
## Applications
- 🚨 **Disaster Response:** Real-time flood, fire, hurricane monitoring
- 🌾 **Precision Agriculture:** Continuous crop health tracking
- 🌍 **Climate Intelligence:** Deforestation and environmental monitoring
- 🏙️ **Urban Planning:** Infrastructure development analysis
- 🛡️ **Defense & Security:** All-weather situational awareness
- 🌊 **Maritime Monitoring:** Vessel tracking and coastal analysis
## Performance Metrics
- **mIoU Accuracy:** 86.66%
- **Improvement over SOTA:** +5.56 percentage points
- **Model Downloads:** 10,000+
- **Active Researchers:** 3,000+
- **Benchmark:** GeoBench (ESA-endorsed)
## Research
**Paper:** DARN: Dynamic Adaptive Residual Network for SAR-to-Optical Translation
**Status:** Under review at CVPR 2026
**Innovation:** Novel adaptive decoder architecture
## Links
- 🌐 **Website:** https://axionorbital.space
- 📚 **MCP Docs:** https://docs.axionorbital.space
- 💻 **GitHub:** https://github.com/axion-orbital
- 🔬 **Research Paper:** [Coming Soon]
- 💬 **Discord:** https://discord.gg/axion-orbital
## Citation
```bibtex
@inproceedings{axion2026,
title={DARN: Dynamic Adaptive Residual Network for SAR-to-Optical Translation},
author={Axion Orbital Team},
booktitle={CVPR},
year={2026}
}
```
---
## ⚠️ TL;DR
- **Axion = Foundation Model** (the neural network)
- **AWS Raw Inference = Research Access** (raw outputs only)
- **MCP Server = Complete Platform** (everything you need)
**For production use, AI agent integration, and the full platform experience:**
```bash
npm install @axion-orbital/mcp-server
```
**The exclusive Axion foundation model with all processing and analysis capabilities is ONLY available through the hosted MCP server.**
---
Made with 🛰️ by [Axion Orbital](https://axionorbital.space)