Building detection

Source	SPOT, Pléiades or Pléiades Neo tasking or catalog collections
Compatibility	- The STAC item should be a SPOT, Pléiades or Pléiades Neo image of the display radiometric processing level - The STAC item should have been added to storage in 2023 or later
Required parameters	- Console: If your chosen input data meets the above specifications you will be able to run your job - API: You will need to input an Output title and the Input data
Output data format	GeoTIFF file: maps the probability of each pixel being part of a building
Output	The result will be added to your account as a STAC item in a new STAC collection. You can retrieve the resulting data in one of the following ways: - Open the console, go to Data management → Jobs - Retrieve the results via the API

Algorithm performance and training data

The algorithm uses Spacept’s custom computer vision model (deep neural network with an Augmented Feature Pyramid Architecture) trained on thousands of Pléiades images where buildings have been semantically annotated. The model accuracy ranges from 80 to 90% depending on input image resolution and location. This is real-world accuracy following benchmarking against helicopter-based LIDAR. Benchmarked against human annotators on 10,000+ Pléiades images the model showed an accuracy of up to 90% depending upon the area. It is constantly improved with subsequent updates. The model was trained using labeled images taken from but not limited to parts of the United States, Mexico, France, Belgium, Portugal, India, Nepal, Indonesia, and Australia.

Further information

For more information, please visit the technical documentation.