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 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-buildings-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: |