Use the model

You can use this model in the Classify Pixels Using Deep Learning tool available in the Image Analyst toolbox in ArcGIS Pro. Follow the steps below to use the model for classifying land cover in images.

Recommended imagery configuration

The recommended imagery configuration is as follows:

• Imagery—Sentinel-2 imagery
• Resolution—10 meters
• Supported configurations are as follows:
  • Sentinel-2 L2A imagery (recommended)—Bottom Of Atmosphere product in the form of a raster, mosaic dataset, or image service.
  • Sentinel-2 L1C imagery—By creating a mosaic using the Manage Sentinel-2 imagery tool, you can use the Multispectral layer with the processing template set to None.

Classify land cover

Complete the following steps to classify land cover 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 Land Cover Classification (Sentinel-2) model and add the imagery layer in ArcGIS Pro and zoom to an area of interest.

   

3. Browse to Tools on the Analysis tab.

   

4. 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.

   

5. 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. Processing Mode—Select the Process as mosaicked image mode.
   5. 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 graphic card.
      • predict_background—If set to True, background class is also classified.
      • test_time_augmentation—Performs test time augmentation while predicting. If true, predictions of flipped and rotated variants of the input image will be merged into the final output.
      • tile_size—The width and height of image tiles into which the imagery is split for prediction.
      • output_label_level—The Corine Land Cover (CLC) level of the output labels. The default value is 2 for CLC level 2 (15 land-cover classes). Use 1 for CLC level 1 (5 land-cover classes).
      • sentinel_imagery_level—The processing level of input Sentinel-2 MSI imagery. The default value is 2 for Level-2A imagery. For processing Level-1C imagery, use 1.
      • merge_classes—Applicable only if output_label_level is 2. If True, Marine waters and Inland waters are merged to create the Waters class and Maritime wetlands and Inland wetlands are merged to create the Wetlands class. Alternatively, set it to False to make them separate classes.
      • 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.)

   

6. 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.

   

7. Click Run.

   The output layer is added to the map.