The algorithm identifies all pixels that are part of a tree in SPOT, Pléiades, or Pléiades Neo display imagery. The result is a GeoTIFF file that maps the probability of each pixel being part of a tree.
Tree detection can be used for infrastructure vegetation risk monitoring, urban planning, and construction.
An input SPOT image of Paihuano (Chile)
A tree probability map
See more on the marketplace.
The algorithm has been developed using tens of thousands of semantically annotated images.
The algorithm has achieved an F1 score of 93%. For optimal results, the algorithm must be used to detect medium or large trees in full foliage. You can use it to detect medium or large trees with less foliage, but the same level of performance isn’t guaranteed.
The STAC item must be a supported data product:
- SPOT catalog: Display
- Pléiades catalog: Display
- Pléiades Neo 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-trees-spacept 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} |