The idea of cleaning up space was once considered science fiction. Today, it has become one of our most urgent engineering and policy challenges in the space age.
As thousands of new satellites launch each year, orbits become even more congested. The European Space Agency estimates there are now over 40,000 trackable objects larger than 10 cm and millions of smaller fragments moving at up to 28,000 km per hour. Even a paint fleck at that speed can impact a spacecraft.
Removing debris from orbit is essential if we are to preserve space as a shared and sustainable environment. But before debris can be captured, nudged, or deorbited, it must first be found, tracked, and understood. The knowledge of where objects are, what they are doing, and how they are changing comes from space situational awareness (SSA) data.
At the heart of every cleanup mission lies tracking data and analysis. And the better that data is, the safer and more successful orbital remediation becomes.
From awareness to action
For decades, space safety has focused on avoidance: monitoring potential collisions and manoeuvring active satellites out of harm’s way.
Now, attention is shifting to active debris removal (ADR), which involves physically capturing defunct hardware and removing it from orbit. Projects such as ClearSpace-1, Astroscale’s ELSA-M, and OrbitGuardians’ ADRAS-J are demonstrating technologies that can grapple, tow, or deorbit dead satellites.
Yet no matter how advanced the robotics or propulsion systems become, every removal mission begins with SSA. Operators must know where their target is, how it moves, and its condition before they can safely approach. In this sense, SSA is not just a support function – it is the foundation of cleanup.
Seeing before cleaning
High-quality SSA data answers three critical questions:
Location: Where exactly is the target, and how is its orbit evolving?
Behaviour: Is it tumbling, drifting, or stable?
Environment: What other objects are nearby that could interfere with a removal attempt?
Optical networks such as Spaceflux’s global telescope array provide continuous, high-precision tracking across low Earth orbit (LEO), geostationary orbit (GEO), and even cislunar space.
Each telescope measures an object’s brightness, motion, and orientation over time, producing a detailed signature that reveals its size, shape, rotation, and stability.
This data enables removal teams to determine the safest approach trajectory and anticipate how the object might respond to capture. Without this foundation, even the most sophisticated debris-removal spacecraft would operate in the dark.
The need for precision
In orbital cleanup, accuracy is everything. A difference of just a few metres in prediction can make the difference between a successful capture and a costly failure. Optical SSA data, refined by AI-based analytics, can track objects with sub-arcsecond precision, far exceeding the positional accuracy of traditional two-line element (TLE) data.
AI models process raw sensor data, filtering out atmospheric noise and continuously updating orbital solutions. They can:
Forecast debris motion influenced by atmospheric drag or radiation pressure.
Detect fragmentation events that alter an object’s spin or mass distribution.
Recommend optimal approach windows for safe rendezvous and capture.
These predictive capabilities transform debris removal from an experimental concept into an operational reality.
Building a collaborative ecosystem
No single organisation can tackle the debris challenge alone. The emerging orbital-sustainability ecosystem depends on partnerships between data providers, removal operators, regulators, and insurers.
SSA providers such as Spaceflux deliver the continuous data streams and predictive models that underpin mission planning.
ADR operators use those insights to design capture trajectories and ensure mission safety.
Agencies and insurers rely on SSA data to verify that objects have been removed and to quantify risk reduction.
By standardising data formats and sharing access through secure interfaces, this collaboration ensures that all actors work from a common operating picture. It is a model of open coordination that balances commercial competition with collective responsibility.
Technology at work
Recent years have seen rapid progress in removal technologies. Different concepts suit different kinds of debris:
Robotic arms or magnetic docking plates for large, intact satellites.
Nets and harpoons for irregular or spinning fragments.
Laser nudging or ion-beam shepherding to push objects without physical contact.
Drag sails and tethers to accelerate re-entry once capture is complete.
Each of these options depends on precise SSA data. A laser-nudging platform, for instance, must know the exact rotation rate and surface reflectivity of its target to deliver just the right impulse. A robotic capture vehicle needs accurate models of angular momentum to avoid destabilising itself during docking.
In all cases, SSA provides the parameters that make the mission safe – a silent but essential partner in every ADR mission.
Accountability and the economics of cleanup
SSA data also plays a critical role in policy, insurance, and economics. As orbital-sustainability regulations evolve, operators will increasingly need to verify compliance, demonstrating that debris has been deorbited safely and that mission activities have reduced collision risk.
ADR also opens the door to new commercial models in which governments or consortia might pay per kilogram of debris removed, with verification by third-party SSA observation. In such a framework, data becomes not just an enabler but a currency of accountability.
The road ahead: Integrated stewardship
Looking ahead, the boundary between monitoring and action will continue to blur. Next-generation cleanup missions will operate alongside real-time SSA networks, drawing on continuous data feeds to autonomously adjust trajectories.
As these systems mature, the focus will shift from individual missions to a persistent, integrated approach, a living data infrastructure that monitors, predicts, and supports active orbital remediation at scale.
To make this possible, the space community will need trusted, transparent, and interoperable data standards, ensuring that every satellite operator, sensor network, and ADR provider speaks the same technical language.
Only then can orbital cleanup move from demonstration to routine practice.
Conclusion: Data as the foundation of cleanup
Cleaning up space is not only an engineering challenge; it is an information challenge. The real difficulty lies not in the mechanics of capture but in the knowledge that precedes it, knowing precisely where debris is, how it behaves, and how it is changing.
As the space economy expands, SSA will remain the foundation of all remediation efforts. From the first optical detection to the final verification of re-entry, data ensures that orbital cleanup is guided by informed decision-making rather than uncertainty.
Space is vast, but precision is what will keep it safe. Through the intelligent use of data and collaboration across the industry, we can move from merely tracking debris to truly cleaning and sustaining the orbital environment for generations to come.