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AI-Assisted Satellite Mapping of Shallow Water Shoals

Ecology · Safety & Security

What it collects

About a place
Anonymized data
  • Satellite imagery of coastal and inland water bodies in Canada, capturing the geographic extent and bathymetric features of shoal areas.
About a measurement
Anonymized data
  • Satellite sensor measurements (e.g. spectral reflectance, radar backscatter) used to detect water depth and shoal characteristics from orbit.
Run by
Canadian Space Agency (CSA)
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

EcologySafety & Security

This system uses artificial intelligence to map and monitor shallow underwater areas (shoals) using satellite imagery, supporting navigation safety and environmental monitoring. It is operated by the Canadian Space Agency and was developed by Effigis Geo-Solutions. The primary users are Government of Canada employees. This is a Phase 1 system, meaning it is in an early operational stage.

What it collects and what happens to it

Data taken in

About a place
Anonymized data
  • Satellite imagery of coastal and inland water bodies in Canada, capturing the geographic extent and bathymetric features of shoal areas.
About a measurement
Anonymized data
  • Satellite sensor measurements (e.g. spectral reflectance, radar backscatter) used to detect water depth and shoal characteristics from orbit.

Processing

Computer Vision
  • Applies computer vision techniques to multispectral or radar satellite imagery to detect, segment, and classify shoal features at scale across Canadian waters.

What it does

Sensing (Perceptive AI)
Human decides
  • The AI processes raw satellite imagery to detect, classify, and map shoal features. Outputs are structured detections delivered to GC employees who make operational decisions.
Deciding (Analytical AI)
Human decides
  • The AI classifies and scores satellite data to determine the presence, extent, and change of shoals over time, producing analytical outputs for human review.

Outputs

About a place
Anonymized data
  • Maps and geospatial datasets showing the location, extent, and changes in shoals over time, delivered to GC employees for navigation safety and environmental planning.
About a measurement
Anonymized data
  • Derived metrics on shoal characteristics such as area, depth estimates, and change detection scores produced from satellite sensor analysis.

Run by

Canadian Space Agency (CSA)
  • Government of Canada department accountable for deploying this AI system to map and monitor shoals via satellite imagery.

Government of Canada AI Register — 2526-CSA-ASC-009

Built by

Effigis Geo-Solutions
  • Private geospatial technology vendor contracted to develop the AI system for satellite-based shoal mapping and monitoring.

Government of Canada AI Register — 2526-CSA-ASC-009

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Output maps and monitoring data are available to Canadian Space Agency employees and other Government of Canada employees designated as primary users.
Not available to me
  • This system is intended for GC employees. Members of the public do not have direct access to the AI system or its outputs through this deployment.

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 TransparencyThis AI system is listed on the Government of Canada's Algorithmic Impact Assessment Register, which provides public disclosure of AI use by federal departments. The register entry is publicly accessible at open.canada.ca.
  • Right to Be Informed of AI UseThe Government of Canada discloses the use of this AI system through its public AI register. GC employees who use the system's outputs should be informed that an AI system contributed to the data they are working with.

Risks and safeguards

  • Reputational harmInaccurate shoal detection could lead to erroneous maps that misrepresent geographic features, potentially undermining trust in official Canadian hydrographic data.Safeguard: Outputs are reviewed by GC employees before operational use; Phase 1 designation indicates ongoing validation against authoritative sources.
  • Physical harmErrors in shoal mapping could contribute to navigational hazards if inaccurate charts are used by mariners.Safeguard: The system is operated by GC employees (not directly by mariners), outputs are subject to human review, and the Phase 1 scope limits deployment until accuracy is validated.