🛰️ SentinelX Tactical AI

Planetary Intelligence for the Next Era

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🌎 Overview

SentinelX Tactical AI is a state-of-the-art, multi-modal satellite intelligence platform designed for real-time planetary monitoring and tactical analysis. By fusing high-revisit optical imagery (Sentinel-2) with all-weather Synthetic Aperture Radar (Sentinel-1), SentinelX provides 24/7 visibility into global changes, infrastructure development, and environmental shifts.

Built for scalability and precision, the platform leverages a hybrid Go/Python backend, NEO4J-powered knowledge graphs, and 3D Digital Twin visualizations via MapLibre/CesiumJS.

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🚀 Key Capabilities

🔷 Multi-Modal Sensor Fusion

• Optimal Optical Analysis: Sentinel-2 (Level 2A) multi-spectral processing for vegetation index (NDVI), water stress, and infrastructure growth.
• SAR Penetration: Sentinel-1 Synthetic Aperture Radar integration for night-time and cloud-covered visibility.
• Tactical Change Detection: Self-supervised Transformer models (Swin-V2) for identifying pixel-level variances between revisits.

🔷 High-Performance Real-Time Engine

• Go-Powered Streaming: Distributed backend for low-latency telemetry ingestion.
• Task Orchestration: Celery workers with GPU-affinity for heavy inference pipelines.
• Spatial Intelligence: PostGIS for complex geospatial queries and AOI tracking.

🔷 AI-Driven Knowledge Graph

• Entity Relation: Tracking objects across Earth's surface and linking them to a Neo4j-based temporal knowledge graph.
• Predictive Analytics: Forecasting territorial changes based on historical patterns.

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🏗️ System Architecture

graph TD
    subgraph "Satellite Data Ingestion"
        S2[Sentinel-2 Optical] --> SH[SentinelHub API]
        S1[Sentinel-1 SAR] --> SH
    end

    subgraph "Tactical AI Core"
        SH --> AI[AI Processing Engine]
        AI --> CD[Change Detection]
        AI --> OD[Object Detection]
        CD --> KV[Knowledge Graph / Neo4j]
    end

    subgraph "Data Persistence"
        KV --> PG[PostGIS / PostgreSQL]
        PG --> RD[Redis Cache]
    end

    subgraph "Interface & Visualization"
        RD --> API[FastAPI / Go Hybrid API]
        API --> FE[Modern Frontend / React]
        FE --> 3D[3D Digital Twin / CesiumJS]
    end

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🛠️ Technical Stack

• Backend: Python 3.11 (FastAPI), Go 1.21
• Intelligence: PyTorch, Swin-Transformer V2, Neo4j
• Geospatial: PostGIS, SentinelHub, Mapbox
• Frontend: Vite, React, MapLibre GL, Tailwind CSS
• Infrastructure: Docker, Kubernetes, Kafka, Redis

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⚡ Quick Start

1. Prerequisite Configuration

Ensure you have API credentials for SentinelHub and Mapbox.

# Clone the repository
git clone https://github.com/shafayat83/SentinelX.git
cd SentinelX

# Configure environment
cp .env.example .env
# Edit .env with your credentials

2. Deployment via Docker Compose

One command to spin up the entire tactical stack.

docker-compose up -d --build

Access the dashboard at http://localhost:3000 and the API documentation at http://localhost:8000/docs.

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🗺️ Visionary Roadmap: Phase 3 & Beyond

• Starshield Tactical Integration: Direct low-latency downlink with Starlink-connected ground stations.
• On-Orbit Edge Computing: Deploying lightweight detection models directly to satellite hardware.
• Autonomous Drone Coordination: Triggering real-time drone reconnaissance upon change detection.
• High-Fidelity 4D Reconstruction: Temporal 3D reconstruction of urban environments.

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⚖️ License

Proprietary & Confidential. All rights reserved. Inspired by the future of multi-planetary exploration and defense.

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"Monitoring Earth to reach the Stars."

S e n t i n e l X