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Shadow Detection

Finds shadows of objects in the image and for each pixel returns the probability of it to be a part of a shadow.

Description

Shadow Detection identifies all pixels corresponding to shadows 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 shadow. 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 Urban Planning & Construction, Solar Power Planning, Land Use and Management, Economic Development Tracking.

Note: This block currently can be used with Pléiades or SPOT Streaming followed by the Raster Tiling Block. Future data block integrations will come in the next versions. Contact UP42 if you have suggestions for which data blocks should be prioritized first.

General InformationDescription
Block TypeProcessing for Shadow Detection
Supported input dataRaster Tiled (512x512) GeoTIFF images.
Output data formatGeoTIFF heat map image containing the probability for each pixel to be part of a shadow.
ResolutionSame resolution as the input data.
PerformanceThe 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

Video tutorial

We have created a 5-minute demonstration on running an analysis process using the Spacept Shadow Detector Machine Learning model, which is accessible on YouTube here.

Example Input

{
   "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
   },
   
   "shadow-detection:1":{
   }
}

Capabilities

To know more please check the block capabilities specifications.

Terms & Conditions

View the End User License Agreement conditions.

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