The algorithm identifies all pixels that are part of a building 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 building.
Building detection can be used for urban planning, fire risk estimation, and land use management.
See more on the marketplace.
The algorithm has been developed using semantically annotated imagery captured over the following countries:
- United States
- Mexico
- France
- Belgium
- Portugal
- India
- Nepal
- Indonesia
- Australia
The accuracy ranges from 80% to 90% depending on input image resolution and location. For optimal results, the algorithm should be applied to imagery captured over the listed territories. You can apply it to similar areas, but the same level of performance isn’t guaranteed.
The STAC item should be a SPOT, Pléiades, or Pléiades Neo image of the display radiometric processing level.
The STAC item should 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.
A sample input payload for the process
JSON
{
"inputs":{
"title": "Building detection over Spain",
"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} |