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 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-shadows-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: |