README.md

Ununennium Documentation

Welcome to the Ununennium documentation. This comprehensive guide covers all aspects of the library, from installation to advanced model deployment.

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Documentation Map

graph TB
    subgraph "Getting Started"
        QS[Quickstart Tutorial]
        INST[Installation]
    end
    
    subgraph "Core Concepts"
        ARCH[Architecture]
        DM[Data Model]
        PIPE[Pipelines]
    end
    
    subgraph "API Reference"
        CORE[Core API]
        IO[Data I/O]
        MODELS[Models]
        TRAIN[Training]
    end
    
    subgraph "Tutorials"
        T1[Ingest & Tiling]
        T2[Train/Val/Test]
        T3[Inference at Scale]
        T4[Change Detection]
        T5[Super-Resolution]
        T6[GAN Recipes]
        T7[PINN Recipes]
    end
    
    subgraph "Advanced Topics"
        GUIDES[Guides]
        RESEARCH[Research]
    end
    
    QS --> ARCH
    ARCH --> API
    API --> Tutorials
    Tutorials --> GUIDES

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Quick Navigation

Section	Description	Audience
Architecture	System design and data flow	All users
API Reference	Complete API documentation	Developers
Tutorials	Step-by-step learning paths	New users
Guides	Best practices and deep dives	Intermediate
Research	Mathematical foundations	Researchers

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Architecture

Understand the design and components of Ununennium.

• System Overview - High-level architecture, component interactions
• Data Model - GeoTensor, GeoBatch, CRS handling
• Pipelines - Data flow from disk to GPU
• Performance - Benchmarks and optimization
• Security and Privacy - Data handling best practices

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API Reference

Detailed documentation for every public class and function.

• Overview - API design principles
• Core - GeoTensor, GeoBatch, types
• Data I/O - COG, STAC, Zarr readers
• Preprocessing - Indices, normalization
• Training - Trainer, callbacks
• Models - Model registry, architectures
• Evaluation - Metrics, validation
• GAN - Generative adversarial networks
• PINN - Physics-informed networks

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Tutorials

Step-by-step guides from beginner to advanced.

Tutorial	Duration	Description
00. Quickstart	15 min	Zero to trained model
01. Ingest and Tiling	30 min	Data preparation
02. Train/Val/Test	45 min	Proper experimental design
03. Inference at Scale	30 min	Production deployment
04. Change Detection	45 min	Multi-temporal analysis
05. Super-Resolution	45 min	Resolution enhancement
06. GAN Recipes	60 min	Image translation
07. PINN Recipes	60 min	Physics-constrained learning

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Guides

Best practices and detailed topic coverage.

• Datasets and Splits - Spatial cross-validation
• Reproducibility - Deterministic experiments
• Configuration - Config file reference
• Benchmarking - Performance testing
• Metrics - Metric selection guide
• Uncertainty and Calibration - Confidence estimation
• Troubleshooting - Common issues

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Research

Mathematical and theoretical foundations.

• Math Foundations - Core mathematical concepts
• Remote Sensing Task Taxonomy - Task classification
• GAN Theory - Adversarial learning dynamics
• PINN Theory - Physics-constrained optimization
• Bibliography - Curated references

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Getting Help

• Troubleshooting Guide - Common issues and solutions
• GitHub Issues - Bug reports
• GitHub Discussions - Questions