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How is deep learning used in ArcGIS Pro?
Deep learning tools in ArcGIS Pro allow you to use more than the standard machine learning classification techniques. Use convolutional neural networks or deep learning models to detect objects, classify objects, or classify image pixels. Integrate external deep learning model frameworks, such as TensorFlow, PyTorch, and Keras.
How to use deep learning in image classification?
Tools in the Image Classification, Mensuration, and Tools groups are now available to you. A new contextual tab Raster Layer with Appearance and Data tabs are activated. In the Image Classification group, click Classification Tools and choose Label Objects for Deep Learning.
How is deep learning used to assess palm tree health?
Inventorying and assessing the health of each palm tree on the Kolovai, Tonga, plantation would take a lot of time and a large workforce. To simplify the process, you’ll use a deep learning model in ArcGIS Pro to identify trees, then calculate their health based on a measure of vegetation greenness.
How does ArcGIS Pro work with Kolovai palms?
ArcGIS Pro works on a contextual basis, so certain tools and tabs will only be available to you if the associated data is selected from the Contents pane. To activate the imagery analysis tools, a raster layer must be selected. In the Contents pane, make sure Kolovai Palms is selected.
How are training samples generated in ArcGIS Pro?
Available with Spatial Analyst license. In supervised image classification, you need to train the classifier to assign pixels or objects to a given class using training samples. The class categories are determined by your classification schema, and the training samples can be generated using the Training Samples Manager pane.
How are class samples used in deep learning?
Deep learning class training samples are based on small subimages, called image chips, that contain the feature or class of interest. Use your existing classification training sample data or GIS feature class data, such as a building footprint layer, to generate image chips containing the class sample from the source image.
How to create training data for deep learning?
This tool will create training datasets to support third-party deep learning applications, such as Google TensorFlow, Keras, PyTorch, and Microsoft CNTK. Deep learning class training samples are based on small subimages, called image chips, that contain the feature or class of interest.