← Marketplace

Tree Detection and Height From Shadow

Finds trees location and their height in the input image.

Description

Tree Detection and Height From Shadow finds trees location and their height in the input image. 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.

The block runs on images with ground sampling distance (GSD) of 0.55m or less. It takes as input Pleiades Display (recommended) or SPOT Display Data Blocks.

The blocks generates the following outputs:

  1. A GeoTIFF heat map image containing the probability for each pixel to be part of a tree.
  2. A shapefile containing the height for each detected tree (meters).

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.

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

Approximation of the cost & performance parameters for the current block version (actual data depends on the region and data block used):

Pléiades Display Area (sqkm)TimeData CreditsInfrastructure CreditsTree-Height-Block Credits
13 min390381260
1030 min390038012600
1005 hours390003800126000
100050 hours390000380001125000

The use cases include Infrastructure Vegetation Risk Monitoring, Urban Planning & Construction.

Supported workflows

Data platform:

Future data blocks integrations will come in next versions – please reach out with the data block you would like to use and Spacept will prioritize its development.

General InformationDescription
Block TypeProcessing for Tree Detection and Height calculation
Supported input dataRaster Tiled GeoTIFF images. The image is expected to have Ground Sampling Distance (GSD) less than 0.55 m.
Output data formatGeoTIFF image for tree probability and GeoJSON for height measurements.
ResolutionSame resolution as the input data for the tree probability and less than 5 meters on the ground for the polygons for height measurements.
PerformanceFor tree height calculation, 1-2 meters accuracy on isolated objects (entire shadow on level ground), up to 8 meters in worse conditions.

Workflow configuration

When setting the tile_height and tile_width for the raster tiles the actual size of the area of interest must be taken into account. Too large raster tiles lead to a larger credit consumption.

Example Job Parameters for Data blocks

{
   "tiling:1":{
      "nodata":null,
      "tile_width":512,
      "tile_height":512,
      "match_extents":false,
      "output_prefix":"",
      "augmentation_factor":1,
      "discard_empty_tiles":true
   },
   "tree-height-detection:1":{
      
   },
   "oneatlas-pleiades-aoiclipped:1":{
      "ids":null,
      "bbox":[
         -1.039205,
         46.732618,
         -1.026172,
         46.743978
      ],
      "time":"2018-01-01T00:00:00+00:00/2020-12-31T23:59:59+00:00",
      "limit":1,
      "zoom_level":18,
      "time_series":null,
      "max_cloud_cover":100,
      "panchromatic_band":false
   }
}

More information

For more information about this block, please check the provider website.

Capabilities

To know more please check the block capabilities specifications.

Terms & conditions

View the End User License Agreement conditions.

Ready to get started?

Explore UP42 now.

Sign in or create an account