Multi-Domain AI Infrastructure Site Selection Engine — LEO · Moon · Mars
When Earth runs out of power and land, where does the next rack go?
Space Machine evaluates optimal locations for AI data centers across three domains using physics-based models, real NASA data, and interactive 3D visualization.
| Domain | Sites | Altitude | Latency | Launch $/kg |
|---|---|---|---|---|
| 🛰️ LEO | 6 orbital configs | 400–1,200 km | 1–10 ms | ~$100 |
| 🌕 Moon | 8 surface sites | — | 1.28 sec | ~$400 |
| 🔴 Mars | 8 surface sites | — | 3–22 min | ~$800 |
| # | Dimension | Formula | Data Source |
|---|---|---|---|
| 1 | Solar Power | solar_flux × availability × 100 |
NASA LRO/LOLA, Mars MOLA |
| 2 | Cooling | εσA(T⁴_hot − T⁴_cold) + convective |
LRO Diviner, Mars climate |
| 3 | Communication | earth_comm_availability × 100 |
Geometric + relay model |
| 4 | Terrain | (1 − surface_difficulty) × 100 |
LOLA DEM, Mars HiRISE |
| 5 | Resources | resource_proximity × 100 |
LCROSS, MRO radar |
| 6 | Radiation | (1 − exposure) × 100 |
LRO CRaTER, Mars RAD |
| 7 | Cost (TCO) | 9-component 10-year model | Starship economics |
Final Score = Σ(score_i × weight_i) / Σ(weight_i) — weights vary by workload type.
# Run full analysis (all domains)
python run.py
# LEO only
python run.py --domain leo
# Moon with batch training weights
python run.py --domain moon --type batch_training
# Mars with $5B budget
python run.py --domain mars --budget 5e9
# Terminal output only (no 3D visualization)
python run.py --no-vizThe interactive visualization uses CesiumJS with real NASA terrain data:
- Earth/LEO — Bing Maps satellite imagery + orbital ring polylines
- Moon — NASA LRO Moon Terrain (asset 2684829, 1m polar resolution)
- Mars — NASA MOLA + Viking colorized imagery (asset 3644333)
- Get a free Cesium Ion token at ion.cesium.com/signup
- Add Moon Terrain and Cesium Mars from the Asset Depot
- Run with token:
python run.py --cesium-token YOUR_TOKEN
# or
export CESIUM_TOKEN=YOUR_TOKEN
python run.pyNote: The visualization must be served via HTTP (not
file://) for Cesium workers to load.run.pystarts a local server automatically.
🥇 Starlink-Adjacent Orbit [🛰️ LEO] 78/100 $261M
🥈 Equatorial LEO [🛰️ LEO] 76/100 $259M
🥉 ISS Orbit [🛰️ LEO] 74/100 $261M
4. Connecting Ridge [🌙 MOON] 74/100 $330M
5. Shackleton Crater Rim [🌙 MOON] 73/100 $337M
10-year TCO with 9 cost components per site:
| Component | LEO | Moon | Mars |
|---|---|---|---|
| Launch cost/kg | $100 | $400 | $800 |
| Payload capacity | 150,000 kg | 50,000 kg | 100,000 kg |
| Operations/yr | $8M | $12M | $20M |
| Rad-hardening | 2.5–5× | 2.5–5× | 2.5–5× |
| Cooling | Radiative (free) | Radiative (free) | Radiative + CO₂ convective |
space-machine/
├── run.py # CLI entry point
├── spacemachine/
│ ├── models.py # SpaceSite, Workload, SiteScore
│ ├── sites.py # 22 candidate sites (LEO/Moon/Mars)
│ ├── physics.py # Domain-specific physics & cost models
│ ├── scoring.py # 7-dimension weighted scoring engine
│ └── visualize.py # CesiumJS 3D HTML generator
├── generate_ppt.py # Goldman Sachs-style PPT generator
├── Space_Machine_Demo.pptx # Presentation deck
└── space_machine_analysis.html # Generated 3D visualization
| Constant | Value | Source |
|---|---|---|
| Solar constant | 1,361 W/m² | NASA Solar Irradiance |
| Stefan-Boltzmann | 5.67×10⁻⁸ W/(m²·K⁴) | NIST CODATA 2018 |
| Mars solar fraction | 43.1% of Earth | 1.524 AU distance |
| Lunar PSR temp | ~40 K | LRO Diviner |
| Laser comm bandwidth | 1.2 Gbps (Moon), 0.1 Gbps (Mars) | NASA LCRD |
| GCR dose | ~0.7 mSv/day | LRO CRaTER |
- Python 3
- CesiumJS — 3D globe visualization
- NASA LRO/LOLA, MOLA, Viking, MRO mission data
- python-pptx — Presentation generation
Built at SemiAnalysis x FluidStack Hackathon, March 2026.
"You solved Dirt to Decode on Earth. We extended it to the stars."