Choose image used to classify pixels

The input image to classify.

It can be an image service URL, a raster layer, or an image service layer.

Choose deep learning model used to classify pixels

The input deep learning package ( .dlpk) item.

The deep learning package is composed of the Esri model definition JSON file ( .emd), the deep learning binary model file, and optionally, the Python raster function to be used.

Specify deep learning model arguments

The function arguments are defined in the Python raster function class referenced by the input model. This is where you list additional deep learning parameters and arguments for experiments and refinement, such as a confidence threshold for adjusting the sensitivity.

The names of the arguments are populated by the tool from reading the Python module on the raster analysis server.

Processing Mode

Specifies how all raster items in a mosaic dataset or an image service will be processed. This parameter is applied when the input raster is a mosaic dataset or an image service.

• PROCESS_AS_MOSAICKED_IMAGE — All raster items in the mosaic dataset or image service will be mosaicked together and processed. This is the default.
• PROCESS_ITEMS_SEPARATELY — All raster items in the mosaic dataset or image service will be processed as separate images.

Result layer name

The name of the layer that will be created in My Content and added to the map. The default name is based on the tool name and the input layer name. If the layer already exists, you will be prompted to provide another name.

You can specify the name of a folder in My Content where the result will be saved using the Save result in drop-down box. If you have the privileges to create both tiled and dynamic imagery layers, you can specify which layer type to generate in the output using the Save result as drop-down box.