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🧠 AI Layer Integration: Direct AI Training
See how our LBVM satellite layers connect directly to AI systems for bulk training, RLHF reward modeling, and foundation model enhancement.
🏗️ Enterprise Integration & Deployment
Real-time infrastructure deployment and live connector setup
Data Centre Deployment
Deploying satellite layer processing infrastructure
Live Verification System
Initializing real-time satellite data verification
Live Connectors
Establishing connections to satellite data sources
Distributed Storage
Configuring multi-region data storage
API Integration Ready
All systems operational and ready for production
Sentinel-2 Optical
Multi-spectral optical imagery from ESA Sentinel-2 constellation
Landsat-8 Thermal
Thermal infrared data from Landsat-8 TIRS sensor
Sentinel-1 SAR
C-band Synthetic Aperture Radar from ESA Sentinel-1
SRTM Elevation
Digital Elevation Model from Shuttle Radar Topography Mission
MODIS Climate
Atmospheric and climate variables from Terra/Aqua MODIS
Large Bio Vision Model (LBVM) Embeddings
AI-derived 64-dimensional embeddings from Large Bio Vision Model with advanced spectral physics and quantum mechanics analysis.
How Large Bio Vision Model (LBVM) Works
Physics-first processing pipeline with realistic sensors, indices, and QA. No web scraping.
Large Bio Vision Model (LBVM) Advanced Processing Pipeline
Scientific Methods & Formulas
NDVI = (NIR - RED) / (NIR + RED) • Sentinel-2: NIR=B8 (0.842μm), Red=B4 (0.665μm) MNDWI = (GREEN - SWIR) / (GREEN + SWIR) • GREEN=B3 (0.56μm), SWIR=B11/B12 (1.61/2.19μm) Brightness Temperature (T, Kelvin) L_λ = ML * Qcal + AL // radiance (sensor-specific) T = K2 / ln( K1 / L_λ + 1 ) // Landsat TIRS constants K1, K2 Surface Reflectance (ρ) ρ = f(TOA radiance, atmospheric transmittance, path radiance, sun angle) • Corrected using 6S/Sen2Cor-like models and ozone/water vapour estimates
We use cloud masks (SCL/Fmask), BRDF-aware compositing, and per-biome thresholds. SAR is despeckled and can include VV/VH polarization features. Thermal bands are converted to brightness temperature before higher-level features.
Limitations, QA, and Realism
- Index thresholds vary by biome and season (e.g., NDVI water/vegetation boundaries)
- Clouds, haze, and shadows require careful masking; residual artifacts possible
- Thermal data has coarser resolution; sub-pixel hot-spots can be diluted
- SAR layover/shadow complicates mountainous urban analysis
- All outputs carry QA flags; embeddings validated via cross-sensor checks
- Evidence hashing available for auditability of served tiles
🔬 Scientific Specifications
Sentinel-2 Optical - Technical Specifications
Scientific Basis:
Spectral signatures provide physics-based ground truth for training robust vision-language models and preventing hallucinations
Example Query:
Generate labeled training data for vision transformers using spectral indices as supervision signals
Applications:
Computer Vision Training Data
Multimodal AI Enhancement
Foundation Model Grounding
Synthetic Dataset Generation
Wavelengths:
Landsat-8 Thermal - Technical Specifications
Scientific Basis:
Thermal emissions provide objective temperature measurements for reward signal generation in reinforcement learning
Example Query:
Train reward models using thermal signatures to align AI responses with physical reality
Applications:
RLHF Reward Modeling
Anomaly Detection Training
Continual Learning Adaptation
Model Interpretability
Wavelengths:
Sentinel-1 SAR - Technical Specifications
Scientific Basis:
All-weather radar signatures provide independent verification channel for cross-validating AI model outputs
Example Query:
Cross-validate LLM claims about surface conditions using weather-independent SAR backscatter analysis
Applications:
Hallucination Detection
Cross-Modal Validation
Embedding Verification
Post-Training Assessment
Wavelengths:
SRTM Elevation - Technical Specifications
Scientific Basis:
Precise elevation measurements provide geometric constraints for training spatially-aware AI models
Example Query:
Create fine-tuning datasets with elevation-based spatial reasoning tasks for foundation models
Applications:
Fine-tuning Dataset Creation
Spatial Reasoning Training
Topological AI Enhancement
Geometric Understanding
Wavelengths:
MODIS Climate - Technical Specifications
Scientific Basis:
Atmospheric variables provide temporal context for training sequence models and long-term dependency learning
Example Query:
Train temporal transformers using atmospheric time series for climate-aware AI reasoning
Applications:
Temporal AI Training
Climate Model Validation
Long-term Memory Systems
Atmospheric Context Enhancement
Wavelengths:
Large Bio Vision Model (LBVM) Embeddings - Technical Specifications
Scientific Basis:
64-dimensional embedding space with electromagnetic resonance physics, quantum mechanics analysis, and advanced spectroscopy using transformer architectures
Example Query:
Align foundation model embeddings with 64-dimensional LBVM space for physics-constrained multimodal reasoning
Applications:
Foundation Model Pre-training
Embedding Space Alignment
Multi-modal Fusion
Physics-constrained AI
Zero-shot Learning