Tree Detection and Height From Shadow

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):

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

Supported workflows

Data platform:

• Catalog -> Pléiades Display or SPOT 6/7 Display -> Order and get the asset from storage of UP42
• In Projects workflows, Processing from Storage -> DIMAP GeoTIFF conversion -> Raster Tiling -> Tree Detection and Height From Shadow (recommended)

Data blocks:

• Pléiades Display (Streaming) or SPOT 6/7 Display (Streaming) -> Raster Tiling -> Tree Detection and Height From Shadow (recommended)

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.

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.