Aircraft detection

An algorithm that detects and classifies aircraft in Pléiades imagery.


Overview

The algorithm uses object detection in Pléiades display imagery to detect and classify multiple types of aircraft. The result is a GeoJSON file with vertically oriented polygons drawn around detected aircraft.

This process detects and classifies multiple types of aircraft:

  • Carrier aircraft
  • Fighter aircraft
  • Helicopters
  • Small aircraft
  • Other

Aircraft detection can be used for aircraft management, airport monitoring, and environmental modeling.

An input Pléiades image of Heathrow Airport (UK)

Detected aircraft polygons drawn on the input image


See more on the marketplace.

Performance

The algorithm has been developed using 2,500 satellite images across 350 air bases in 50 countries spanning 6 continents. The algorithm counted 54,000 aircraft to create a historical baseline of activity and detect ongoing changes.

For optimal results, the algorithm must be applied to imagery captured over airport areas. You can apply it to similar areas, but the results might not be as accurate.

Scenarios represented in the training data:

  • Different times of day
  • Different times of year
  • Different terrains
  • Different object configurations

Requirements for input imagery

The data item must be a supported data product:

The data item must be CNAM-compatible. Check that the data item has been added to storage in 2023 or later.

Input parameters

You must specify the data items you want to apply the process to.

You must specify the title of the output data item.

API input

Use the detection-aircraft-oi name ID for the processing API.

JSON
{
"inputs": {
"title": "Processing imagery over Berlin",
"item": "https://api.up42.com/v2/assets/stac/collections/21c0b14e-3434-4675-98d1-f225507ded99/items/23e4567-e89b-12d3-a456-426614174000"
}
}
ParameterOverview
inputs.title

object | required

The title of the output data item.

inputs.item

object | required

A link to the data item in the following format: https://api.up42.com/v2/assets/stac/collections/{collection-id}/items/{item-id}.


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