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Large Language Models for Space Domain Awareness

Leveraging Large Language Models for Space Domain Awareness

The increasing complexity of space operations has brought new challenges to space domain awareness (SDA). As the number of active satellites, debris, and potential collision risks grows, efficient tools for analyzing and summarizing space object data are critical. Large Language Models (LLMs), such as GPT, can be transformative tools for interpreting, contextualizing, and acting on vast datasets in the space domain.

Large Language Models in SDA: An Overview

LLMs are trained on diverse datasets and excel at processing natural language, summarizing data, and answering questions. Their application in SDA can bridge the gap between technical orbital data and actionable insights, enabling both experts and non-experts to make informed decisions.

Here, we explore their potential in:

  1. Question Answering: Interpreting complex SDA data and providing concise, user-friendly explanations.
  2. Characterization and Summarization: Extracting key information about space objects, including their orbits, operational status, and potential risks.
  3. Orbital Data Comparison: Using formats like Two-Line Element sets (TLE) to analyze and compare the trajectories of multiple space objects.

Decoding Orbital Data with LLMs

Two-Line Element Sets (TLEs) are a standard format for describing the orbits of space objects. Each TLE provides key parameters, such as inclination, eccentricity, and mean motion, which define the object’s trajectory around Earth. While TLEs are fundamental to orbital analysis, their technical nature makes them challenging for non-specialists (e.g., space intelligence analysts) to interpret.

LLMs can simplify this by:

  • Parsing TLEs: Converting raw orbital parameters into plain-language descriptions. For example, an LLM could translate a TLE into: “This satellite is in a low Earth orbit (LEO), with an inclination of 51.6 degrees, meaning it passes over most of the Earth’s surface.”
  • Answering Orbit-Related Questions: Providing explanations to queries like, “What is the closest approach between Satellite A and Satellite B?” or “How does the orbit of Object X compare to that of Object Y?”
  • Visualizing Trajectories: While LLMs can’t generate visuals directly, they can describe orbital paths in terms that make subsequent visualization easier for other tools.

SpaceGPT Use Case: Comparing Orbits of Space Objects

Spectronn's SpaceGPT is a domain-specific LLM fined tuned for SDA. It has multiple capabilities, we provide one example next.

Suppose we have TLE data for two space objects:

  1. Satellite A:
    • Inclination: 98 degrees (sun-synchronous orbit)
    • Eccentricity: 0.001 (nearly circular orbit)
    • Altitude: 700 km
  2. Satellite B:
    • Inclination: 56 degrees
    • Eccentricity: 0.02 (slightly elliptical orbit)
    • Altitude: 500 km

SpaceGPT can:

  1. Summarize the Orbits:
    • Satellite A: "This satellite is in a sun-synchronous orbit, ideal for Earth observation as it passes over the same regions at consistent times. Its nearly circular orbit ensures stable imaging conditions."
    • Satellite B: "This satellite operates at a lower altitude with a slightly elliptical orbit, which may result in varying imaging conditions. Its inclination covers a more limited range of latitudes."
  2. Identify Differences:
    • "Satellite A’s higher altitude gives it a broader field of view, but it covers less detail compared to Satellite B. The differing inclinations indicate that Satellite A focuses on global coverage, while Satellite B prioritizes mid-latitude regions."
  3. Flag Potential Risks:
    • "Given their altitudes and inclinations, these satellites are unlikely to collide but may experience conjunction events if perturbations alter their trajectories."

LLMs for Space Object Characterization

Beyond TLE analysis, LLMs can integrate additional datasets, such as radar tracking or optical observations, to:

  • Characterize Space Objects: Summarizing information about an object’s size, material composition, and mission purpose.
  • Assess Anomalies: Explaining irregular behavior, such as sudden changes in orbit due to propulsion or collisions.
  • Summarize Events: Providing concise reports on orbital maneuvers, conjunctions, or re-entries.

Ensuring Accuracy and Trust

While LLMs hold immense potential for SDA, ensuring their outputs are reliable is critical. This involves:

  • Training on High-Quality Data: Using curated datasets from trusted space agencies and organizations.
  • Incorporating Expert Feedback: Validating LLM outputs with domain experts to refine models and improve accuracy.
  • Enhancing Transparency: Combining LLM outputs with explainability tools to show how conclusions are drawn.

The Future of LLMs in SDA

As the volume and complexity of space data grow, LLMs will become indispensable tools for interpreting and acting on SDA insights. By democratizing access to orbital analysis and making space intelligence more comprehensible, LLMs can empower a broader range of stakeholders—from policymakers to commercial operators—to contribute to the sustainable use of space.

In this evolving landscape, collaboration between AI developers, space agencies, and private entities will be key to maximizing the potential of LLMs while maintaining safety, transparency, and trust.