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Helpful Places

Government of Canada – DTPR AI Register

AI-Powered Geospatial Imagery Analysis for Government

Research & Development · Planning & Decision-making

What it collects

About a measurement
Anonymized data
  • Open Earth observation datasets including Sentinel and Landsat satellite imagery, elevation models, geographic coordinates, time tags, and spectral information from sensors. These are environmental measurements with no personal identifiers.
About a place
Anonymized data
  • Licensed high-resolution imagery covering Canadian and other geographic areas, including descriptive texts and other attributes attached to Earth observation data, used to train the geospatial foundation model.
Run by
Natural Resources Canada (NRCan)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Research & DevelopmentPlanning & Decision-making

This system is a foundation AI model developed by Natural Resources Canada to process and represent large volumes of satellite and aerial imagery for use across federal geospatial applications. It learns how to describe Earth observation data — such as satellite images, elevation models, and sensor readings — so that other AI tools built on top of it can work more efficiently. The system is currently used by Government of Canada employees and is not disclosed to end-users as an AI system.

What it collects and what happens to it

Data taken in

About a measurement
Anonymized data
  • Open Earth observation datasets including Sentinel and Landsat satellite imagery, elevation models, geographic coordinates, time tags, and spectral information from sensors. These are environmental measurements with no personal identifiers.
About a place
Anonymized data
  • Licensed high-resolution imagery covering Canadian and other geographic areas, including descriptive texts and other attributes attached to Earth observation data, used to train the geospatial foundation model.

Processing

Classification & Prediction
  • Representation learning algorithms that learn how to describe and encode geospatial Earth observation data into vector embeddings. The model does not interpret data directly but produces descriptive representations that downstream classifiers and predictors can use.

What it does

Sensing (Perceptive AI)
Human decides
  • The model perceives and encodes raw Earth observation data — satellite imagery, elevation models, spectral sensor readings — into structured representations. Downstream analysts and AI systems then interpret these representations; humans decide how the outputs are used.
Deciding (Analytical AI)
Human decides
  • The foundation model uses representation learning to generate descriptive embeddings and classifications from geospatial data. These structured outputs are advisory — human analysts or downstream AI systems use them for further decision-making.

Outputs

About a measurement
Anonymized data
  • Structured geospatial embeddings and descriptive representations derived from Earth observation data — including imagery encodings, elevation descriptors, and spectral feature vectors — made available to downstream geospatial AI systems and GC employees. No personal data is included.

Run by

Natural Resources Canada (NRCan)
  • Natural Resources Canada (NRCan) is the federal department that develops and deploys this geospatial foundation model, making it available to Government of Canada employees for geospatial AI applications.

Canadian Geospatial Foundation Model — Government of Canada AI Register

Built by

Government of Canada
  • The Government of Canada developed this system internally. No external vendor is identified in the register entry.

Canadian Geospatial Foundation Model — Government of Canada AI Register

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Outputs and model representations are available to Natural Resources Canada and, as a foundation model, to other Government of Canada departments and employees who build downstream geospatial AI solutions.
Not available to me
  • The model's outputs and internal representations are not accessible to members of the public. The system is used internally by Government of Canada employees and AI use is not disclosed to end-users.

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

  • Right to Algorithmic TransparencyThe Government of Canada AI Register entry for this system is publicly accessible. However, the register notes that AI use is not disclosed to users of downstream systems. Members of the public may consult the register at the source URL for information about the system's general logic and purpose.

Risks and safeguards

  • Civil liberties harmThe system processes environmental Earth observation data with no personal data in scope; however, geospatial foundation models could be applied downstream to enable surveillance or tracking of individuals or groups. The register does not disclose mitigation measures. Risk is medium given the foundation-model architecture, which enables diverse downstream applications whose impacts may not be fully anticipated at the time of this disclosure.