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AI-Assisted Satellite Tasking Optimization for Space Operations

Logistics · Planning & Decision-making

What it collects

Operational data
Anonymized data
  • Satellite tasking requests, imaging schedules, satellite availability windows, observation parameters, and operational constraints used as inputs to optimize task assignment.
Run by
Canadian Space Agency (CSA)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization, Vendor

What it is for

LogisticsPlanning & Decision-making

This system uses machine learning to autonomously optimize the tasking of satellite observation requests for the Canadian Space Agency. It determines how to schedule and assign satellite imaging jobs without manual intervention. The primary users are Government of Canada employees working in space operations. This is Phase 1 of a vendor-developed solution built by SkyWatch Space Applications Inc.

What it collects and what happens to it

Data taken in

Operational data
Anonymized data
  • Satellite tasking requests, imaging schedules, satellite availability windows, observation parameters, and operational constraints used as inputs to optimize task assignment.

Processing

Optimization
  • Machine learning-based optimization algorithm that searches for the best assignment and schedule of satellite tasking requests under operational constraints such as satellite availability, imaging windows, and priority weightings.

What it does

Deciding (Analytical AI)
Human decides
  • The system scores, ranks, and schedules satellite tasking requests using machine learning. GC employee operators review and act on the system's recommendations, keeping human oversight of final operational decisions.
Acting (Agentic AI)
Human executes
  • The system autonomously plans and executes multi-step satellite tasking workflows — decomposing a set of imaging requests into an optimized schedule and dispatching assignments without manual intervention for each step. Phase 1 focuses on this autonomous workflow execution.

Outputs

Operational data
Anonymized data
  • Optimized satellite tasking schedules and task assignments produced by the machine learning system, indicating which imaging requests are assigned to which satellites at which times.

Run by

Canadian Space Agency (CSA)
  • Federal government department responsible for space operations in Canada. Deploys and operates the autonomous satellite tasking optimization system for internal Government of Canada employees.

Government of Canada AI Registry — Automatize tasking

Built by

SkyWatch Space Applications
  • The vendor responsible for developing the autonomous tasking optimization machine learning system on behalf of the Canadian Space Agency.

Government of Canada AI Registry — Automatize tasking

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Tasking outputs and system data are accessible to the Canadian Space Agency and GC employees who operate and use the system. External public access is not indicated.
Available to vendor
  • SkyWatch Space Applications Inc. as the developing vendor may have access to system data for maintenance, model improvement, and support purposes, though the register does not specify the scope of vendor data access.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

The Helpful Places has not listed specific rights for this system.

Risks and safeguards

No risks or safeguards have been published for this system yet.