The World's First Spatiotemporal Knowledge Graph for Predictive Earth Intelligence
AstroGraph represents the pinnacle of 2026 Earth Intelligence, functioning as a high-performance Spatiotemporal Knowledge Graph (STKG) that indexes the physical world. Built on a decentralized architecture, it integrates petabytes of satellite imagery from constellations like Sentinel, Planet, and Maxar with real-time IoT and socioeconomic data. Technically, it utilizes Graph Neural Networks (GNNs) and Vision Transformers (ViTs) to convert unstructured raster data into structured, queryable entities. By the mid-2020s, AstroGraph has transitioned from a specialized defense tool to a foundational AI layer for global supply chains and climate risk assessment. It enables developers to perform 'Physical RAG'—providing Large Language Models with grounded, real-time spatial facts. Its architecture supports multi-modal fusion, allowing for the simultaneous analysis of hyperspectral data, SAR (Synthetic Aperture Radar), and ground-level telemetry. As a market leader, AstroGraph positions itself as the 'Google for Physical Assets,' providing the indexing layer for the global digital twin economy.
AstroGraph represents the pinnacle of 2026 Earth Intelligence, functioning as a high-performance Spatiotemporal Knowledge Graph (STKG) that indexes the physical world.
Explore all tools that specialize in change detection. This domain focus ensures AstroGraph (by Astraea) delivers optimized results for this specific requirement.
Uses H3 and S2 geometry libraries to index temporal sequences of 3D data across a graph structure.
Generative AI models that convert Synthetic Aperture Radar (SAR) data into human-interpretable optical views.
Native connectors for vector databases to ground LLM responses in verifiable geospatial facts.
On-the-fly identification of ships, planes, tanks, and vegetation using specialized ViT architectures.
Synchronizes distilled graph nodes to edge devices for low-latency field operations.
Calculates the delta between graph states at T1 and T2 to identify structural changes.
Analyzes chemical signatures of materials on the ground via spectral band decomposition.
Provision an Astraea Cloud account and verify enterprise domain.
Generate an OAuth2 Client ID and Secret through the Developer Console.
Define your AOI (Area of Interest) using a GeoJSON polygon or bounding box.
Select data catalogs, ranging from open-source Sentinel-2 to high-res commercial imagery.
Initialize the AstroGraph SDK in a Python/Jupyter environment.
Configure the Graph Schema to define relationships between physical assets (e.g., Factory -> Port -> Route).
Launch an automated 'Insight Pipeline' to extract features using pre-trained Computer Vision models.
Perform a temporal query to analyze asset changes over a specific historical window.
Connect the AstroGraph output to an LLM via the provided LangChain/LlamaIndex integration.
Deploy the pipeline as a scheduled job with Webhook notifications for anomaly detection.
All Set
Ready to go
Verified feedback from other users.
"Users praise the platform for its ability to handle massive datasets without performance degradation, though some note a steep learning curve for the GraphQL-based spatial query language."
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