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Classify Objects Using Deep Learning

Description

Classify Objects Using Deep Learning diagram

The ClassifyObjectsUsingDeepLearning task is used to classify objects based on overlaid imagery data using the designated deep learning model and generate a feature service with a new assigned label for each object.

License:
As of 10.8, you must license your ArcGIS Server as an ArcGIS Image Server to use this resource.

Request parameters

ParameterDetails
inputRaster

(Required)

The image that will be classified. This can be specified as the portal item ID, image service URL, cloud raster dataset, shared raster dataset, a feature service with image attachments, or a raster dataset or image collection in the data store. At least one type of input must be provided in the JSON object. If multiple inputs are given, the itemId takes priority.

Syntax: A JSON object describes the input raster.

Example:

//Portal Item ID
inputRaster={"itemId": <portal item id>}

//Image Service URL
inputRaster={"url": <image service url>}

//Feature Service URL
inputRaster={"url": <feature service url>}

//URI for a cloud raster or shared data path
inputRaster={"uri": <cloud raster uri or shared data path>}

//Service Properties
inputRaster={"serviceProperties":{"name":" testrasteranalysis ","serviceUrl":"https://<server name>/server/rest/services/Hosted/testrasteranalysis /ImageServer"},"itemProperties":{"itemId":"8cfbd3ec25584d0d8f4ed23b8ff7c43b", "folderId":"sdfwerfbd3ec25584d0d8f4"}}

//Data store URI 
inputRaster={"uri":"/rasterStores/rasterstorename/A/B/C"}
or
inputRaster={"uri":"/fileShares/filesharedatastorename/A/B/C"}
or
inputRaster={"uri":"/cloudStores/cloudstorename/A/B/C"}
inputFeatures

(Optional)

The feature service layer that contains points, polylines, or polygons that identify the location of each object to be classified and labeled. The layer index is needed for the feature service URL.

Syntax: A JSON object describes the input feature service layer.

Example:

//Feature Service Layer URL
inputFeatures={"url": <feature service layer url>}

//Shared Data Path
inputFeatures={"uri": <shared data path>}

//Service Properties
inputFeatures={"serviceProperties":{"name":"testrasteranalysis","serviceUrl":"https://<server name>/server/rest/services/Hosted/testrasteranalysis/FeatureServer/0"},"itemProperties":{"itemId":"8cfbd3ec25584d0d8f4ed23b8ff7c43b","folderId":"sdfwerfbd3ec25584d0d8f4"}}

Example

inputFeatures={"url": "https://myserver/arcgis/rest/services/Hosted/testrasteranalysis/FeatureServer/0}
outputFeatureClass

(Required)

The output hosted feature service properties. If the hosted feature service is already created, the portal item ID or service URL can be provided, and 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.

Syntax: A JSON object describes the output feature service.

Example:

//Portal Item ID
outputFeatureClass={"itemId": <portal item id>}

//Hosted Feature Service URL
outputFeatureClass={"url": <hosted feature service url>}

//Feature Class Local Output Path
outputFeatureClass={"uri": <feature class local output path>}

//Service Properties
outputFeatureClass={"serviceProperties":{"name":"testrasteranalysis","serviceUrl":"https://<server name>/server/rest/services/Hosted/testrasteranalysis/FeatureServer"},"itemProperties":{"itemId":"8cfbd3ec25584d0d8fed23b8ff7c43b","folderId":"sdfwerfbd3ec25584d0d8f4"}}
model

(Required)

The deep learning model to use to classify objects. This can be specified as the deep learning model portal item ID, as an .emd or .dlpk file, or as the entire JSON string of the model definition.

Syntax: A JSON object describes the model.

Example:

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

//.emd or .dlpk file
model={"uri": "\\\\sharedstorage\\sharefolder\\ClassifyHouseDamage.emd"}
model={"uri": "\\\\sharedstorage\\sharefolder\\ClassifyHouseDamage.dlpk"}
model={"uri": "/rasterStores/rasterstorename/A/B/ClassifyHouseDamage.emd"}
model={"uri": "/rasterStores/rasterstorename/A/B/ClassifyHouseDamage.dlpk"}

//.emd or .dlpk file stored in raster store with file share type
model = {"uri": "/fileShares/filesharedatastorename/A/B/ClassifyHouseDamage.emd"}
model={"uri": "/fileShares/filesharedatastorename/A/B/model.dlpk"}

Example for JSON:

model={
  "Framework": "Keras",
  "ModelConfiguration": {
    "Name": "KerasClassifier"
  },
  "ModelFile": "Damage_Classification_Model.h5",
  "ModelType": "ObjectClassification",
  "ImageHeight": 256,
  "ImageWidth": 256,
  "ExtractBands": [
    0,
    1,
    2
  ],
  "CropSizeFixed": 1,
  "BlackenAroundFeature": 1,
  "ImageSpaceUsed": "MAP_SPACE",
  "Classes": [
    {
      "Value": 0,
      "Name": "undamaged",
      "Color": [
        255,
        255,
        0
      ]
    },
    {
      "Value": 1,
      "Name": "damaged",
      "Color": [
        0,
        255,
        255
      ]
    }
  ]
}
modelArguments

(Optional)

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

Syntax: A JSON object describes the value pairs of arguments.

Example:

modelArguments={"name1": "value1", "name2": "value2"}
classLabelField

(Optional)

The name of the field that will contain the classification label in the output feature service.

Syntax: String.

Example:

classLabelField="ClassLabel"
processAllRasterItems

(Optional)

Specifies how all raster items in an image service will be processed. If set to true, all raster items in the image service will be processed as separate images. If set to false, all raster items in the image service will be mosaicked together and processed. This is the default.

Values: true | false

Example:

processAllRasterItems=false
context

(Optional)

Environment settings that affect task execution. This task has the following settings:

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

Example

context={"cellSize": "20", "processorType": "CPU", "parallelProcessingFactor": "4"}
f

The response format. The default response format is html.

Values: html | json

Example usage

The following is a sample request URL for ClassifyObjectsUsingDeepLearning:

https://machine.domain.com/webadaptor/rest/services/System/RasterAnalysisTools/GPServer/ClassifyObjectsUsingDeepLearning?
inputRaster="url":"https://<server name>/arcgis/ArcGIS/rest/services/World_Imagery/MapServer"&inputFeatures="url":"https://<server name>/arcgis/server/rest/services/Hosted/test_parkinglot/FeatureServer/0"&outputFeatureClass={"serviceProperties":{"name":"test10210453"}}&model={"itemId": "d8d3902b41854529a907ad9f42af5a06"}&modelArguments={"padding": "0", "batch_size": "16"}&classLabelField=ClassLabel&processAllRasterItems=false&context={"extent": {"xmin": -13160539.4563053,"ymin": 3998752.62631951,"xmax": -13160427.5538234,"ymax": 3998824.51069532,"spatialReference": {"wkid": 3857}},"processorType": "CPU","parallelProcessingFactor": 2}}&f=json

The following is a sample POST request for ClassifyObjectsUsingDeepLearning:

POST /webadaptor/rest/services/System/RasterAnalysisTools/GPServer/ClassifyObjectsUsingDeepLearning HTTP/1.1
Host: machine.domain.com
Content-Type: application/x-www-form-urlencoded
Content-Length: []

inputRaster={"url":"https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer"}&inputFeatures={"url":"https://<server name>/arcgis/server/rest/services/Hosted/test_parkinglot/FeatureServer/0"}&outputFeatureClass={"serviceProperties": {"name":"test10210453"}}&model={"itemId": "d8d3902b41854529a907ad9f42af5a06"}modelArguments={"padding": "0", "batch_size": "16"
}classLabelField=ClassLabel&processAllRasterItems=false&context={"extent": {"xmin": -13160539.4563053,"ymin": 3998752.62631951,"xmax": -13160427.5538234,"ymax": 3998824.51069532,"spatialReference": {"wkid": 3857}},"processorType": "CPU", "parallelProcessingFactor": 2}}&f=json

Both of the above requests use the following parameters and values in their request:

inputRaster={"url":"https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer"}
iputFeatures={"url":"https://<server name>/arcgis/server/rest/services/Hosted/test_parkinglot/FeatureServer/0"}
outputFeatureClass={"serviceProperties":{"name":"test10210453"}}
model={"itemId": " d8d3902b41854529a907ad9f42af5a06"}
modelArguments={"padding": "0", "batch_size": "16"}
classLabelField=ClassLabel
processAllRasterItems=false
context={"extent": {"xmin": -13160539.4563053,"ymin": 3998752.62631951,"xmax": -13160427.5538234,"ymax": 3998824.51069532,"spatialReference": {"wkid": 3857}}, "processorType": "CPU", "parallelProcessingFactor": 2}}
f=json

Response

When you submit a request, the task assigns a unique job ID for the transaction.

Syntax:

{ "jobId": "<unique job identifier>", "jobStatus": "<job status>" }

After the initial request is submitted, you can use the jobId to periodically check the status of the job and messages, as described in Check job status. Once the job has successfully completed, use the jobId to retrieve the results. To track the status, you can make a request of the following form:

https://<rasterAnalysisTools-url>/AddImage/jobs/<jobId>

When the status of the job request is esriJobSucceeded, you can access the results of the analysis by making a request of the following form:

https://<rasterAnalysisTools-url>/AddImage/jobs/<jobId>/results/outObjects

JSON Response example

The response returns the outObjects output parameter, which has properties for parameter name, data type, and value. The content of value is always the output feature layer itemId and the image service URL.

{
  "paramName": "outObjects",
  "dataType": "GPFeatureRecordSetLayer",
  "value": {
    "itemId": "f121390b85ef419790479fc75b493efd",
    "url": "https://<server name>/arcgis/rest/services/Hosted/<service name>/FeatureServer"
  }
}