Label | Explanation | Data Type |
From Raster
| The input raster before the change. | Raster Dataset; Raster Layer; Mosaic Layer; Image Service; Map Server; Map Server Layer; Internet Tiled Layer |
To Raster
| The input raster after the change. | Raster Dataset; Raster Layer; Mosaic Layer; Image Service; Map Server; Map Server Layer; Internet Tiled Layer |
Output Classified Raster
| The output classified raster that shows the change. | Raster Dataset |
Model Definition | 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. | File; String |
Arguments (Optional) | The information from the Model Definition parameter will be used to populate this parameter. These arguments vary, depending on the model architecture. The following are supported model arguments for models trained in ArcGIS. ArcGIS pretrained models and custom deep learning models may have additional arguments that the tool supports.
| Value Table |
Available with Image Analyst license.
Summary
Runs a trained deep learning model to detect change between two rasters.
This tool requires a model definition file containing trained model information. The model definition file can be an Esri model definition JSON file (.emd) or a deep learning model package, and it must contain the path to the Python raster function to be called to process each object and the path to the trained binary deep learning model file.
Usage
The inputs for this tool are two images: an image of a previous time and an image of a more recent time. The output is a classified raster dataset that shows the change between the two raster inputs.
You must install the proper deep learning framework Python API (such as TensorFlow or PyTorch) in the ArcGIS AllSource Python environment; otherwise, an error will occur when you add the Esri model definition file to the tool. Obtain the appropriate framework information from the creator of the Esri model definition file.
To set up your machine to use deep learning frameworks in ArcGIS AllSource, see Install deep learning frameworks for ArcGIS.
This tool calls a third-party deep learning Python API (such as TensorFlow, PyTorch, or Keras) and uses the specified Python raster function to process each object.
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.
Additional input parameters may be necessary such as mini-batch size, padding size, and so on.
See the sample below for a model definition JSON file (.emd).
Sample model definition JSON file
{ "Framework": "", "ModelConfiguration":" ", "ModelFile":"", "InferenceFunction":"", "ModelType":"", "ImageHeight":256, "ImageWidth":256, "ExtractBands":[0,1,2], "CropSizeFixed": 1, "BlackenAroundFeature": 1, "Classes": [ { "Value": 0, "Name": "Building", "Color": [255, 0, 0] } ] }
Increasing the batch size can improve tool performance; however, as the batch size increases, more memory is used. If an out of memory error occurs, use a smaller batch size. The batch_size value can be adjusted using the Arguments parameter.
Batch sizes are square numbers, such as 1, 4, 9, 16, 25, 64 and so on. If the input value is not a perfect square, the highest possible square value is used. For example, if a value of 6 is specified, it means that the batch size is set to 4.
This tool supports and uses multiple GPUs, if available. To use a specific GPU, specify the GPU ID environment. When the GPU ID is not set, the tool uses all available GPUs. This is default.
For information about requirements for running this tool and issues you may encounter, see Deep Learning frequently asked questions.
For more information about deep learning, see Deep learning in ArcGIS AllSource.
Parameters
DetectChangeUsingDeepLearning(from_raster, to_raster, out_classified_raster, in_model_definition, {arguments})
Name | Explanation | Data Type |
from_raster | The input raster before the change. | Raster Dataset; Raster Layer; Mosaic Layer; Image Service; Map Server; Map Server Layer; Internet Tiled Layer |
to_raster | The input raster after the change. | Raster Dataset; Raster Layer; Mosaic Layer; Image Service; Map Server; Map Server Layer; Internet Tiled Layer |
out_classified_raster | The output classified raster that shows the change. | Raster Dataset |
in_model_definition | The in_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. | File; String |
arguments [arguments,...] (Optional) | The information from the in_model_definition parameter will be used to set the default values for this parameter. These arguments vary, depending on the model architecture. The following are supported model arguments for models trained in ArcGIS. ArcGIS pretrained models and custom deep learning models may have additional arguments that the tool supports.
| Value Table |
Code sample
This example runs a deep learning model to find the difference between two images.
# Import system modules
import arcpy
from arcpy.ia import *
# Check out the ArcGIS Image Analyst extension license
arcpy.CheckOutExtension("ImageAnalyst")
DetectChangeUsingDeepLearning("c://detectchange//input_image1.tif",
"c://detectchange//input_image2.tif", "c://detectchange//output_difference.tif",
"c://detectchange/detectBuilding.emd", "padding 0;score_threshold 0.6;batch_size 4")
This example runs a deep learning model to find the difference between two images.
# Import system modules
import arcpy
from arcpy.ia import *
"""
Usage: DetectObjectsUsingDeepLearning(from_raster, to_raster, out_classified_raster,
in_model_definition, {model_arguments})
"""
# Check out the ArcGIS Image Analyst extension license
arcpy.CheckOutExtension("ImageAnalyst")
# Set local variable
from_raster = r"c:/detectchange/input_image1.tif"
to_raster = r"c:/detectchange/input_image2.tif"
out_classified_raster = r"c:/detectchange/output_difference.tif"
in_model_definition = r"c:/ detectchange/detectbuilding.emd"
# arcpy.env.processorType = "GPU"
# arcpy.env.gpuId = 0
# Execute
DetectChangeUsingDeepLearning(from_raster, to_raster, out_classified_raster,
in_model_definition, "padding 0;score_threshold 0.6;batch_size 4")