The algorithm identifies all pixels that are part of a building in SPOT, Pléiades, or Pléiades Neo 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.
An input Pléiades image of Superior, Wisconsin (USA)
A building probability map
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 must 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 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-buildings-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} |