Use the model

You can use this model in the Classify Pixels Using Deep Learning tool available in the ArcGIS Image Analyst toolbox in ArcGIS Pro. Follow the steps below to use the model for cloud mask generation from satellite imagery.

Recommended imagery configuration

The recommended imagery configuration is as follows:

• Imagery—Sentinel-2 imagery
• Resolution—10 meters
• Supported configurations—Sentinel-2 L2A imagery (recommended)—Bottom Of Atmosphere product in the form of a raster, mosaic dataset, or image service

Generate cloud mask

Complete the following steps to generate cloud mask from the imagery:

Data preparation:
1. Prepare data according to the following product type:
   • Raster Product
   1. Browse to the folder housing the Sentinel-2 L2A data. Expand the folder and locate the raster product.
   2. Expand the Raster Product provided as an .xml file and select the BOA Reflectance derived raster dataset.

      

   • Mosaic Dataset
   1. Create a Mosaic Dataset using the Create Mosaic Dataset geoprocessing tool. Set the variables on the Parameters tab as follows:
      • Output Location—Select a geodatabase.
      • Mosaic Dataset Name—Set the mosaic dataset name.
      • Coordinate System—Select a coordinate system for the output mosaic dataset.
      • Product Definition—Select None.

      

   2. To add the raster to the mosaic dataset, open the Add Rasters To Mosaic Dataset geoprocessing tool. Set the variables on the Parameters tab as follows:
      • Mosaic Dataset—Select the input mosaic dataset.
      • Raster Type—Select Sentinel-2 from the drop-down list.
      • Processing Templates—Select BOA Reflectance from the drop-down list.
      • Input Data—Select Folder from the drop-down list, browse and add the .SAFE files.

      

      Note: You can create a multiband image through either the Composite Bands geoprocessing tool or Composite Bands raster function if required, which can be used as input for inferencing.

Data processing:
2. Download the Cloud Mask Generation (Sentinel 2) model and add the imagery layer in ArcGIS Pro.
3. Zoom to an area of interest.
   
4. Browse to Tools on the Analysis tab.

   

5. Click the Toolboxes tab in the Geoprocessing pane, select Image Analyst Tools, and browse to the Classify Pixels Using Deep Learning tool under Deep Learning.

   

6. Set the variables on the Parameters tab as follows:
   1. Input Raster—Select the imagery.
   2. Output Raster Dataset—Set the output feature class that will contain the classification results.
   3. Model Definition—Select the pretrained or fine-tuned model .dlpk file.
   4. Arguments (optional)—Change the values of the arguments if required.
      • padding—Number of pixels at the border of image tiles from which predictions are blended for adjacent tiles. Increase its value to smooth the output while reducing edge artifacts. The maximum value of the padding can be half of the tile size value.
      • batch_size—Number of image tiles processed in each step of the model inference. This depends on the memory of your graphics card.
      • predict_background—The background class is also classified. Set it to True.
      • test_time_augmentation—Performs test time augmentation while predicting. Set it to True.
      • tile_size—The width and height of image tiles into which the imagery is split for prediction.
      • radiometric_offset_correction—Corrects radiometric offset of -1000 in imageries sensed after January 25, 2022, in Sentinel 2 L2A imagery. (Note: Verify whether your data provider has already applied the offset for post January 2022 data, ; sources such as Azure and Copernicus require the radiometric_offset_correction parameter set as True. Set the parameter to False for AWS data, as it is already radiometric offset corrected and doesn't require correction again.)

      

7. Set the variables on the Environments tab as follows:
   1. Processing Extent—Select Current Display Extent or any other option from the drop-down menu.
   2. Cell Size (required)—Set the value to 10.

      The expected raster resolution is 10 meters.

   3. Processor Type—Select CPU or GPU.

      It is recommended that you select GPU, if available, and set GPU ID to specify the GPU to be used.

   

8. Click Run.

   The output layer is added to the map.