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Tree Detection
Finds trees in the image and for each pixel returns the probability map of it to be a part of a tree.
Description
Tree Detection identifies all pixels corresponding to trees in satellite images. The block runs on Pleiades Streaming (recommended) or SPOT Streaming Data Blocks and outputs a shadow mask with the probability of each pixel corresponding to a tree. NB: We recommend using 0.51 for this particular model following in-field tests for powerline inspection applications.
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 Height Detection from Shadows block.
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. We recommend using the Raster Tiling block upstream to speed up processing and reduce infrastructure costs for large areas.
Use cases include Vegetation Management, Urban Planning & Construction, Fire Risk Estimation, Land Use and Management, Forestry, Powerline inspection, Trainline inspection.
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
Data blocks:
- Pléiades Display (Streaming) or SPOT 6/7 Display (Streaming) -> Raster Tiling -> Tree Detection
General Information | Description |
---|---|
Block Type | Processing for Shadow Detection |
Supported input data | Raster Tiled (512x512) GeoTIFF images. |
Output data format | GeoTIFF heat map image containing the probability for each pixel to be part of a tree. |
Resolution | Same resolution as the input data. |
Performance | The model accuracy ranges from 80 to 90% depending on input block resolution and location. This is real-world accuracy following benchmarking against helicopter based LIDAR. Benchmarked against human annotators on 10,000+ Pleiades images the model showed an average precision of 91%, recall of 94.5% and an F1 score of 93%. Please note that for seasonal trees, the block works best when the input images have the trees in full foliage. The model aims to focus on medium to large trees, limiting detection confidence for brushes and ignoring high grass. |
Video tutorial
We have created a 5-minute demonstration on running an analysis process using the Spacept Tree Detector Block, which is accessible on YouTube here.
Example input for Data blocks
{
"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
},
"tiling:1":{
"nodata":null,
"tile_width":512,
"tile_height":512,
"match_extents":false,
"output_prefix":"",
"augmentation_factor":1,
"discard_empty_tiles":true
},
"tree-detection:1":{
}
}
Capabilities
Terms & conditions
View the End User License Agreement conditions.