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AI-Assisted Vegetation Fire Risk Assessment for Rail Lines

Safety & Security · Ecology

What it collects

About a measurement
Anonymized data
  • Drone-mounted lidar (light detection and ranging) sensor readings capturing the physical structure and density of vegetation near rail lines, used to derive dryness and moisture content estimates.
About a place
Anonymized data
  • Georeferenced locations of rail line corridors and surrounding vegetation zones, used to spatially situate the lidar measurements and fire risk assessments.
Run by
Transport Canada (TC)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Not stated by the Helpful Places.

What it is for

Safety & SecurityEcology

This system uses drone-mounted lidar sensors and machine learning algorithms to measure how dry vegetation is near railway corridors, which helps identify areas at elevated risk of fire ignition. It is used internally by Transport Canada employees to support rail safety monitoring. The system does not process personal information — it analyses physical environmental conditions along rail lines.

What it collects and what happens to it

Data taken in

About a measurement
Anonymized data
  • Drone-mounted lidar (light detection and ranging) sensor readings capturing the physical structure and density of vegetation near rail lines, used to derive dryness and moisture content estimates.
About a place
Anonymized data
  • Georeferenced locations of rail line corridors and surrounding vegetation zones, used to spatially situate the lidar measurements and fire risk assessments.

Processing

Classification & Prediction
  • Algorithms that derive vegetation dryness classifications or fire risk scores from processed lidar point-cloud data, using statistical or machine learning models trained on labelled vegetation and dryness observations.

What it does

Deciding (Analytical AI)
Human decides
  • The system scores or classifies vegetation dryness levels from drone lidar data, producing risk indicators that Transport Canada employees review and act upon. Humans make final decisions about any rail safety interventions.

Outputs

About a measurement
Anonymized data
  • Vegetation dryness scores and fire risk assessments for specific rail line corridor segments, derived from the lidar data analysis and presented to Transport Canada employees for safety decision-making.
About a place
Anonymized data
  • Spatially located risk maps or zone-level outputs identifying which sections of railway corridor vegetation present elevated fire risk, enabling geographically targeted maintenance or monitoring interventions.

Run by

Transport Canada (TC)
  • Transport Canada is the federal department responsible for transportation policies, programs, and rail safety. It is the accountable deployer of this vegetation fire risk assessment system, which is used exclusively by its employees.

Government of Canada Algorithmic Impact Assessment Register — 2526-TC-014

Built by

Not stated by the Helpful Places.

Kept for

Not stated by the Helpful Places.

Shared with

Not stated by the Helpful Places.

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 system is listed on the Government of Canada's AI register, which provides public disclosure of its purpose and department. The system processes only environmental sensor data and does not affect individual rights or make decisions about people. For further information about the system or the register, contact Transport Canada.

Risks and safeguards

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