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Tree and tree height detection


An algorithm that detects trees in SPOT or Pléiades imagery and returns a probability map and the height of detected trees. For each pixel, it returns the probability of it to be part of a tree, and computes its height based on the length of its adjacent shadow. Tree and tree height detection can be used for infrastructure vegetation risk monitoring, urban planning, and construction.

Technical information

SourceSPOT, 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 tree
OutputThe 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 managementJobs
- Retrieve the results via the API

Algorithm performance and training data

The solution uses deep learning techniques for computer vision to detect trees and shadows and physics equations to compute the height using sun and satellite position information. For tree height calculation, the performances ranges from 1-2 meters accuracy on isolated objects (entire shadow on level ground), up to 8 meters in worse conditions.

Due to its complexity, this algorithm requires a significant running time. There are no restrictions on image dimensions, but the computing time will increase with the size of the image.

Further information

For more information, please visit the technical documentation.

Terms & conditions

View the End User License Agreement conditions

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