Detect Objects Using Deep Learning

(Required)

The portal item Id, image service URL, cloud raster dataset, or shared raster dataset that will be classified. At least one type of input needs to be provided in the JSON object. If multiple inputs are given, the itemId takes priority.

Syntax: JSON object describes the input raster.

{"itemId": <portal item id>}
{"url": <image service url>}
{"uri": <cloud raster uri or shared data path>}
{"serviceProperties":{"name":"testrasteranalysis",
  "serviceUrl":"http://<server name>/server/rest/services/Hosted/testrasteranalysis/ImageServer"},
  "itemProperties":{"itemId":"8cfbd3ec25584d0d8f4ed23b8ff7c43b", "folderId":"sdfwerfbd3ec25584d0d8f4"}}

(Required)

The output hosted feature service properties. If the hosted feature service is already created, the portal item Id or service URL can be given to the service tool. The output path of the feature class that is generated will be used to update the existing service definition. The service tool can also generate a new hosted feature service with the given service properties.

The output hosted feature service is stored and shared on the hosting server.

{"itemId": <portal item id>}
{"url": <hosted feature service url>}
{"uri": <feature class local output path>}
{"serviceProperties":{"name":"testrasteranalysis",
   "serviceUrl":"https://<server name>/server/rest/services/Hosted/testrasteranalysis/FeatureServer"},
   "itemProperties":{"itemId":"8cfbd3ec25584d0d8fed23b8ff7c43b", "folderId":"sdfwerfbd3ec25584d0d8f4"}
}

(Required)

The input for model can be a model package item uploaded to your portal, an .emd file, or the entire JSON string of the model definition.

Example for portal item:

{"itemId": "x2u130909jcvojzkeeraedf"}
{"url": "https://<portal name>/portal/sharing/rest/content/items/ x2u130909jcvojzkeeraedf"}

Example for .emd file:

{"uri": "\\\\sharedstorage\\sharefolder\\findtrees.emd"}

Example for JSON:

{
    "Framework": "TensorFlow",
    "ModelConfiguration": "ObjectDetectionAPI",
    "ModelFile":"frozen_inference_graph.pb",
    "ModelType":"ObjectDetection",
    "ImageHeight":850,
    "ImageWidth":850,
    "ExtractBands":[0,1,2],
    "Classes": [
      {
        "Value": 0,
        "Name": "Tree",
        "Color": [0, 255, 0]
      }
    ]
}

The name value pairs of arguments and their values that can be customized by the clients.

{"name1": "value1", "name2": "value2"}

Contains additional settings that affect task execution. This task has the following settings:

• Extent (extent)—A bounding box that defines the analysis area.
• Cell Size (cellSize)—The output raster will have the resolution specified by cell size.
• Processor Type (processorType)—The specified processor (CPU or GPU) will be used for the analysis.
• Parallel Processing Factor (parallelProcessingFactor)—The specified number or percentage of processes will be used for the analysis.