The algorithm tiles the input imagery and 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
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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 results might not be as accurate.
The data item must be a supported data product:
- SPOT catalog: Display
- Pléiades catalog: Display
- Pléiades Neo catalog: Display
The data item must be CNAM-compatible. Check that the data item has been added to storage in 2023 or later.
You must specify the data items you want to apply the process to.
You must specify the title of the output data item.
Use the detection-trees-spacept name ID for the processing API.
{ "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 data item. |
inputs.item | object | required A link to the data item in the following format: |