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Shadow detection
Description
An algorithm that detects shadows in SPOT or Pléiades imagery and returns a probability map. It identifies all pixels corresponding to shadows in satellite images. Shadow detection can be used for urban planning, construction, solar power planning, economic development tracking, and land use analysis.
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 shadow |
| 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 model accuracy is approximately 95% for detecting tree shadows. For building shadows, the accuracy of the 1.0 model decreases to approximately 70%, and there are false positives in some water bodies being identified as shadows. Both these limitations will be improved in version 2.0.
The solution uses deep learning techniques for computer vision and is constantly improved with subsequent updates; thus far, it has been trained on tens of thousands of semantically annotated images. The model is used as part of the Tree and tree height detection algorithm.
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.