Shadow detection

An algorithm that detects shadows in SPOT, Pléiades, or Pléiades Neo imagery and returns a probability map.


Overview

The algorithm identifies all pixels that are part of a shadow in SPOT, Pléiades, or Pléiades Neo display imagery. The result is a GeoTIFF file that maps the probability of each pixel being part of a shadow.

Shadow detection can be used for urban planning, construction, solar power planning, and land use analysis.

An input SPOT image of Paihuano (Chile)

A shadow probability map

See more on the marketplace.

Training data and accuracy

The algorithm has been developed using tens of thousands of semantically annotated images.

The accuracy is as follows:

  • For tree shadows: 95%
  • For building shadows: 70%

There might be false positives in some water bodies being identified as shadows.

Requirements for input imagery

The STAC item must be a supported data product:

The STAC item must be CNAM-compatible. Check that the STAC item has been added to storage in 2023 or later.

Input parameters

Required parameters

Input imagery

You need to specify the STAC items you want to apply the process to.

Output title

You need to specify the title of the output objects. This title will be assigned to the resulting STAC item and STAC collection.

API input

Use the detection-shadows-spacept name ID for the processing API.

A sample input payload for the process

JSON

    {
  "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"
  }
}

  
ParameterOverview
inputs.titleobject / required
The title of the output objects: STAC item and STAC collection.
inputs.itemobject / required
The STAC item link in the following format: https://api.up42.com/v2/assets/stac/collections/{collection-id}/items/{item-id}