The algorithm identifies all pixels that are part of a shadow 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 shadow.
Shadow detection can be used for urban planning, construction, solar power planning, and land use analysis.
An input SPOT image of Paihuano (Chile)
A shadow probability map
See more on the marketplace.
The algorithm has been developed using tens of thousands of semantically annotated images.
The accuracy is as follows:
- For tree shadows: 95%
- For building shadows: 70%
There might be false positives in some water bodies being identified as shadows.
The STAC item must be a display data product from one of the following collections: SPOT, Pléiades, or Pléiades Neo.
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-shadows-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} |