← Marketplace
Tree and tree height detection
Description
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
| 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 tree |
| 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 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.