The input raster dataset that will be classified.
The input can be a single raster, multiple rasters in a mosaic dataset, an image service, a folder of images, or a feature class with image attachments.
|Raster Dataset; Raster Layer; Mosaic Layer; Image Service; Map Server; Map Server Layer; Internet Tiled Layer; Folder; Feature Layer; Feature Class|
The Model Definition parameter value can be an Esri model definition JSON file (.emd), a JSON string, or a deep learning model package (.dlpk). A JSON string is useful when this tool is used on the server so you can paste the JSON string rather than upload the .emd file. The .dlpk file must be stored locally.
It contains the path to the deep learning binary model file, the path to the Python raster function to be used, and other parameters such as preferred tile size or padding.
The function arguments are defined in the Python raster function class. This is where you list additional deep learning parameters and arguments for experiments and refinement, such as a confidence threshold for adjusting sensitivity. The names of the arguments are populated from reading the Python module.
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.
The folder where the output classified rasters will be stored. A mosaic dataset will be generated using the classified rasters in this folder.
This parameter is required when the input raster is a folder of images or a mosaic dataset in which all items are to be processed separately. The default is a folder in the project folder.
The feature class where the output classified rasters will be stored.
This parameter is required when the input raster is a feature class of images.
|Label||Explanation||Data Type||Output Raster Dataset|
The name of the raster or mosaic dataset containing the result.