The algorithm uses object detection in Pléiades display imagery to detect and quantify cars. The result is a GeoJSON file with points drawn on detected cars.
Car detection can be used for traffic and parking management, retail analysis, and urban planning.
An input Pléiades image of Berlin (Germany)
Detected car points drawn on the input image
See more on the marketplace.
The algorithm has been developed using 180,000 images in 50 countries spanning 6 continents.
For optimal results, the algorithm must be applied to imagery captured over North America and Europe. You can apply it to similar areas, but the same level of performance isn’t guaranteed. The algorithm has limitations when dealing with highly shadowed imagery, imagery containing closely parked vehicles, and desert imagery.
Scenarios represented in the training data:
- Different times of day
- Different times of year
- Different terrains
- Different object configurations
The STAC item must be a supported data product:
- Pléiades catalog: Display
The STAC item must be CNAM-compatible. Check that the STAC item has been added to storage in 2023 or later.
Required parameters
Input imagery
You need to specify the STAC items you want to apply the process to.
Output title
You need to specify the title of the output objects. This title will be assigned to the resulting STAC item and STAC collection.
Use the detection-cars-oi name ID for the processing API.
A sample input payload for the process
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"
}
}
| Parameter | Overview |
|---|---|
inputs.title | object / required The title of the output objects: STAC item and STAC collection. |
inputs.item | object / required The STAC item link in the following format: https://api.up42.com/v2/assets/stac/collections/{collection-id}/items/{item-id} |