Spectralis AI bridges orbital sensor physics and real-time planetary intelligence. We unify 256 continuous electromagnetic wavebands and polarimetric radar with sub-second space-to-ground edge inference.
Standard satellite imagery is blind to 98% of the electromagnetic reality. Toggle across Spectralis AI's continuous spectral channels to inspect real-time synthetic data extraction.
True-color planetary observation composite. Optimized for visible structural reconnaissance, urban mapping, and visible surface change detection.
Quantized INT8 spatial-spectral tensor operators running directly on radiation-hardened satellite bus processors, achieving 64:1 lossless latent compression prior to RF downlink.
Engineered from first principles for distributed tensor training and high-throughput multi-modal spaceborne inference.
Unlike discrete vision transformers that discard unaligned bands, CSST-1 models the electromagnetic spectrum as a continuous Fourier manifold, ingesting arbitrary bands from 400nm to 14µm.
Fuses active microwave coherent phase radar (X/C-band) with passive optical reflectance, rendering cloud cover and nocturnal darkness completely transparent to mission analysts.
Conditioned on physical radiative transfer equations, our latent diffusion pipeline reconstructs 0.15m tactical ground sampling distance from raw 3m satellite sensor footprints.
Discards raw image bloat at the satellite payload level. Extracts high-density tactical geospatial vectors on orbit, broadcasting critical event triggers directly to end-user ground nodes in under 5 seconds.
CSST-1 requires massively parallel multi-node tensor clusters to ingest trillions of multi-spectral tokens across 256 dimensions. Our training pipeline utilizes mixed-precision FP8/BF16 tensor parallelism, continuous 3D spatial-spectral masked autoencoders, and high-throughput fabric interconnects.
Evaluated against public NASA AVIRIS-NG, ESA Sentinel-2, and commercial Maxar/Planet constellations across standardized remote sensing datasets.
| PERFORMANCE PARAMETER | SPECTRALIS CSST-1 (OURS) | LEGACY RESNET/CONV-NETS | RAW OPTICAL RGB SATELLITES |
|---|---|---|---|
| Continuous Spectral Channels | 256 Bands (Continuous) | 12 Bands (Fixed) | 3 Bands (RGB Only) |
| Event Detection Latency | < 65 milliseconds (Edge) | 4 – 8 Hours (Ground Post-Process) | 12 – 36 Hours (Downlink Delay) |
| Cloud Deck Penetration | 99.98% (Multi-Modal SAR Fusion) | 0% (Completely Obscured) | 0% (Completely Obscured) |
| Methane Fugitive Detection | 15 kg/hr (Facility Level) | 500+ kg/hr (Coarse Regional) | Not Detectable (Wavelength Blind) |
| Downlink Data Compression | 64:1 Lossless Neural Latents | 4:1 JPEG2000 Baseline | None (Full Raw Payload) |
| Effective Ground Sampling (GSD) | 0.15m (Generative Super-Res) | 1.2m – 3.0m Native | 0.5m – 10.0m Native |
12 interactive slides outlining our foundation tech, TAM, unit economics, and distributed compute scaling targets. Use arrow keys or the controls below.
Continuous multi-spectral Earth observation and sub-second orbital sensor fusion for defense, climate, and infrastructure.
Our team combines NASA Jet Propulsion Laboratory mission science, Stanford deep learning research, and commercial aerospace leadership.
Ex-NASA JPL Principal Imaging Scientist. Ph.D. in Computational Imaging from Stanford. 24+ peer-reviewed papers in multi-spectral neural compression and spatial transformers.
Ex-ESA Copernicus AI Fellow. Ph.D. Oxford in Atmospheric Radiative Transfer. Leading pioneer in polarimetric SAR waveband reconstruction.
Ex-Lockheed Martin Space Systems & US Naval Aviator. 18 years in orbital payload integration, satellite constellation operations, and ITAR compliance.
Ex-CERN Large Hadron Collider distributed tensor computing researcher. Specialist in heterogeneous cluster parallelization and radiation-hardened edge kernels.
Direct dispatch to Julian Thorne (Founder & CEO) and our orbital mission operations bench.
Direct dispatch to Julian Thorne (Founder & CEO) and the mission integration flight team.