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What are pretrained deep learning models? What can pretrained models do for you? Instantly extract features from a stream of data Pretrained deep learning models can instantly recognize complex shapes, patterns, and textures at various scales within images, point clouds, or video.

Eliminate the need to develop and train models With pretrained models, you no longer have to invest time and energy into labeling datasets and training your own model.

Retrainable to your geography and imagery Although our pretrained models aren't trained in all geographies, you can adapt them to your particular terrain, geography, and imagery type. Types of models Pretrained deep learning models perform tasks, such as feature extraction, classification, redaction, detection, and tracking, to derive meaningful insights from large amounts of imagery. Image feature extraction and detection Pixel classification Point cloud classification Image redaction Object tracking.

Image feature extraction and detection Extract features, such as buildings, vehicles, swimming pools, and solar panels, from aerial and satellite imagery. Browse models. Pixel classification Classify land-cover satellite imagery. Point cloud classification Classify power lines and tree points using point cloud data.

Image redaction Blur sensitive areas from imagery to comply with privacy policies. Object tracking Track moving objects such as vehicles in motion imagery. How it works. Select your input Point to the imagery you want to extract information from. Extract Browse for your desired pretrained deep learning model and run your analysis. Video Integrated mesh scenes by Systematics Watch a demonstration showing feature extraction of building windows for use in line-of-sight analysis using a pretrained model.

Read the blog. Explore documentation Find information on how pretrained deep learning models can help you complete tasks quickly.

What's new? Stay up to date on releases for new or enhanced pretrained deep learning models. Take a tour of our models Interact with and explore the type of data you can extract using pretrained models. Deep learning blog Read articles in our deep learning blog to understand the extent of featured capabilities available with pretrained deep learning models.

The percentage of training samples that will be used for validating the model. Specifies whether early stopping will be implemented. Specifies whether the backbone layers in the pretrained model will be frozen, so that the weights and biases remain as originally designed. CheckedпїЅThe backbone layers will be frozen, and the predefined weights and biases will not be altered in the Backbone Model parameter.

This is the default. UncheckedпїЅThe backbone layers will not be frozen, and the weights and biases of the Backbone Model parameter can be altered to fit the training samples. This takes more time to process but typically produces better results. The default is [0. This example trains a tree classification model using the U-Net approach. This example trains an object detection model using the SSD approach. Feedback on this topic? Back to Top. Available with Image Analyst license.

Usage This tool trains a deep learning model using deep learning frameworks. SSD is used for object detection. The input training data for this model type uses the Pascal Visual Object Classes metadata format. U-Net is used for pixel classification. Feature classifier Object classification пїЅ The Feature Classifier approach will be used to train the model.

This is used for object or image classification. RetinaNet is used for object detection. MaskRCNN is used for object detection. This approach is used for instance segmentation, which is precise delineation of objects in an image. This model type can be used to detect building footprints. Class values for input training data must start at 1. YOLOv3 is used for object detection. DeepLab is used for pixel classification. FasterRCNN is used for object detection.

This approach is useful to improve edge detection for objects at different scales. This approach is useful to in edge and object boundary detection. The Multi Task Road Extractor is used for pixel classification. This approach is useful for road network extraction from satellite imagery.

ConnectNet is used for pixel classification. Pix2Pix is used for image-to-image translation. This approach creates a model object that generates images of one type to another. The input training data for this model type uses the Export Tiles metadata format. CycleGAN is used for image-to-image translation. This approach is unique in that the images to be trained do not need to overlap. The input training data for this model type uses the CycleGAN metadata format.

Super-resolution Image translation пїЅ The Super-resolution approach will be used to train the model. Super-resolution is used for image-to-image translation. This approach creates a model object that increases the resolution and improves the quality of images.

Change detector Pixel classification пїЅ The Change detector approach will be used to train the model. Change detector is used for pixel classification. This approach creates a model object that uses two spatial-temporal images to create a classified raster of the change. The input training data for this model type uses the Classified Tiles metadata format. Image captioner Image translation пїЅ The Image captioner approach will be used to train the model.

Image captioner is used for image-to-text translation. This approach creates a model that generates text captions for an image. Siam Mask is used for object detection in videos. The model is trained using frames of the video and detects the classes and bounding boxes of the objects in each frame. MMDetection is used for object detection. MMDetection is used for pixel classification. The supported metadata format is Classified Tiles. Deep Sort is used for object detection in videos.

The input training data for this model type uses the Imagenet metadata format. Where Siam Mask is useful while tracking an object, Deep Sort is useful in training a model to track multiple objects. Pix2PixHD is used for image-to-image translation. MaX-DeepLab is used for panoptic segmentation. This approach creates a model object that generates images and features. The input training data for this model type uses the Panoptic metadata format. DETReg is used for object detection.

The input training data for this model type uses the Pascal Visual Object Classes. DenseNet пїЅ The preconfigured model will be a dense network trained on the Imagenet Dataset that contains more than 1 million images and is layers deep.

MobileNet version 2 пїЅ This preconfigured model will be trained on the Imagenet Database and is 54 layers deep geared toward Edge device computing, since it uses less memory. ResNet пїЅ The preconfigured model will be a residual network trained on the Imagenet Dataset that contains more than million images and is 18 layers deep. ResNet пїЅ The preconfigured model will be a residual network trained on the Imagenet Dataset that contains more than 1 million images and is 34 layers deep.

ResNet пїЅ The preconfigured model will be a residual network trained on the Imagenet Dataset that contains more than 1 million images and is 50 layers deep. ResNet пїЅ The preconfigured model will be a residual network trained on the Imagenet Dataset that contains more than 1 million images and is layers deep.

VGG пїЅ The preconfigured model will be a convolution neural network trained on the Imagenet Dataset that contains more than 1 million images to classify images into 1, object categories and is 11 layers deep. VGG with batch normalization пїЅ This preconfigured model will be based on the VGG network but with batch normalization, which means each layer in the network is normalized. It trained on the Imagenet dataset and has 11 layers. VGG пїЅ The preconfigured model will be a convolution neural network trained on the Imagenet Dataset that contains more than 1 million images to classify images into 1, object categories and is 13 layers deep.

It trained on the Imagenet dataset and has 13 layers. VGG пїЅ The preconfigured model will be a convolution neural network trained on the Imagenet Dataset that contains more than 1 million images to classify images into 1, object categories and is 16 layers deep. It trained on the Imagenet dataset and has 16 layers. VGG пїЅ The preconfigured model will be a convolution neural network trained on the Imagenet Dataset that contains more than 1 million images to classify images into 1, object categories and is 19 layers deep.

It trained on the Imagenet dataset and has 19 layers. DarkNet пїЅ The preconfigured model will be a convolution neural network trained on the Imagenet Dataset that contains more than 1 million images and is 53 layers deep.

RESNET18 пїЅ The preconfigured model will be a residual network trained on the Imagenet Dataset that contains more than million images and is 18 layers deep. RESNET34 пїЅ The preconfigured model will be a residual network trained on the Imagenet Dataset that contains more than 1 million images and is 34 layers deep. RESNET50 пїЅ The preconfigured model will be a residual network trained on the Imagenet Dataset that contains more than 1 million images and is 50 layers deep.

RESNET пїЅ The preconfigured model will be a residual network trained on the Imagenet Dataset that contains more than 1 million images and is layers deep.

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Deep learning arcgis pro	The Object Detection exploratory analysis tool uses a trained deep learning here to recognize objects displayed in the current map or scene. Classify Pixels Using Deep Learning. The following deep learning frameworks are supported: TensorFlow Keras PyTorch If your model is learninf using a deep learning framework that is not listed, a custom inference function a Python module is required with the prl model, and you must set InferenceFunction to the Python module path. Esri has released 8 new pretrained geospatial deep learning models for feature extraction workflows. MaX-DeepLab is ge software for panoptic segmentation.
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Use convolutional neural networks or deep learning models to detect objects, classify objects, or classify image pixels. Use a model definition file multiple times to detect change over time or detect objects in different areas of interest. Generate a polygon feature class showing the location of detected objects to be used for additional analysis or workflows.

The creation and export of training samples are done in ArcGIS Pro using the standard training sample generation tools. Once the model is trained, use an Esri model definition file.

You must install the deep learning framework Python packages; otherwise, an error occurs when you add the Esri model definition file to the deep learning geoprocessing tools. For information about how to install these packages, see Install deep learning frameworks for ArcGIS. After using a deep learning model, it's important that you review the results and assess the accuracy of the model.

You can also use the Compute Accuracy For Object Detection tool to generate a table and report for accuracy assessment. To learn about the basics of deep learning applications with computer vision, see Introduction to deep learning. For information about requirements for running the geoprocessing tools, and issues you may encounter, see Deep learning frequently asked questions.

It contains model definition parameters that are required to run the inference tools, and it should be modified by the data scientist who trained the model. There are required and optional parameters in the file as described in the table below. Once the. For example, an. Some parameters are used by all the inference tools; these are listed in the table below.

The name of a deep learning framework used to train your model. The following deep learning frameworks are supported: TensorFlow Keras PyTorch If your model is trained using a deep learning framework that is not listed, a custom inference function a Python module is required with the trained model, and you must set InferenceFunction to the Python module path. The model configuration defines the model inputs and outputs, the inferencing logic, and the assumptions made about the model inputs and outputs.

Existing open source deep learning workflows define standard input and output configuration and inferencing logic. ArcGIS supports the following set of predefined configurations:. If you used one of the predefined configurations, type the name of the configuration in the. If you trained your deep learning model using a custom configuration, you must describe the inputs and outputs in full in the.

The type of model. ImageClassificationпїЅFor classifying pixels ObjectDetectionпїЅFor detecting objects or features ObjectClassificationпїЅFor classifying objects and features. The path to a trained deep learning model file. The file format depends on the model framework. For example, in TensorFlow, the model file is a.

Provide information about the model. Model information can include anything to describe the model you have trained. Examples include the model number and name, time of model creation, performance accuracy, and more. An inference function understands the trained model data file and provides the inferencing logic. If your model is trained using a deep learning model configuration that is not yet supported, or it requires special inferencing logic, a custom inference function a Python module is required with the trained model.

In this case, set InferenceFunction to the Python module path. The name of the sensor used to collect the imagery from which training samples were generated. The number of rasters used to generate the training samples. The number of rows in the image being classified or processed. The number of columns in the image being classified or processed.

The band indexes or band names to extract from the input imagery. Information about the output class categories or objects. The range of data values if scaling or normalization was done in preprocessing. The amount of padding to add to the input imagery for inferencing. The number of training samples to be used in each iteration of the model. The fraction of GPU memory to allocate for each iteration in the model.

The default is 0. The format of the metadata labels used for the image chips. The type of reference system used to train the model. The names given to each input band, in order of band index. Bands can then be referenced by these names in other tools. The following is an example of a model definition file. A deep learning model package. The package can be uploaded to your portal as a DLPK item and used as the input to deep learning raster analysis tools. Deep learning model packages must contain an Esri model definition file.

The trained model file extension depends on the framework you used to train the model. For example, if you trained your model using TensorFlow, the model file will be a.

Depending on the model framework and options you used to train your model, you may need to include a Python Raster Function. You can include multiple trained model files in a single deep learning model package. You can change this location in the Share and download options. Functionality in the package that is not supported at the version of ArcGIS Pro being used to consume the package is not available. Once you've created a sufficient number of features in the Image Classification pane, you'll export them as image chips with metadata.

The map zooms to the first area of sample palm trees that you'll identify. You'll use this tool to draw circles around each palm tree in your current display. Circles are drawn from the center of the feature outward, measuring the radius of the feature.

A new palm record is added in the Labeled Objects group of the Image Classification pane. You'll create a palm record for every tree you can to ensure there are many image chips with all the palm trees marked.

If you would like extra guidance to help you understand how to draw these circles, or if you would like to skip digitizing the trees, a training sample dataset is available in the folder you downloaded. On the ribbon, on the Map tab, in the Layer group, click Add Data. Browse to the Databases folder and double-click the Results geodatabase.

Click PalmTraining and click OK. When you're finished with this first bookmark's extent, you'll have approximately samples recorded in the Training Samples Manager pane. Here are a few details to help you as you identify the trees: You can zoom and pan around the map to make digitizing easier but be sure to digitize as many of the trees within the extent of the bookmark as you can. If you are not sure about the exact location of a tree, it is OK to skip it.

You want to ensure that you create accurate training samples. It is OK if the circles you draw overlap. Your final model will take into account the size of the trees you identify, so be sure to mark both small and large palm trees. Digitizing training samples can be a time-consuming process, but it pays off to have a large number of samples.

The more samples you provide the model with as training data, the more accurate the results will be. As an example, the training dataset used to train the model provided with this lesson had more than samples. Although you saved the training samples to a geodatabase, you need to refresh the geodatabase to be able to access this dataset.

The Catalog pane appears. Your PalmTraining feature class is now visible. The last step before training the model is exporting your training samples to the correct format as image chips. The Geoprocessing pane appears. You'll set the parameters for creating image chips.

First, you'll choose the imagery used for training. Next, you'll create a folder to store the image chips. Next, you'll select the feature class containing the training samples you created. If you did not draw the training samples, a dataset has been provided for you to use. Browse to Databases and open the Results geodatabase. Select PalmTraining and click OK. Next, you'll select the field from your training data that holds the class value for each feature you drew.

Recall that your palm class value was 1. Next, you'll choose the output format for your chips. The format you choose is based on the type of deep learning model you want to train. Next, you'll set the size, in pixels, for each of your image chips. The image chip size is determined by the size of the features you are trying to detect.

If the feature is larger than the tiles' x and y dimensions, your model will not provide good results. Now, you'll ensure that your output format is correct. This, too, is dependent on the type of deep learning model that you are creating. Before you run the tool and create image chips, you'll set the tool's environments.

In particular, you need to know the resolution of the imagery. It's a best practice to create image chips at the same resolution as your input imagery. Depending on your computer's hardware, the tool will take a few minutes to run. The images chips are created and are ready to be used for training a deep learning model. In this module, you downloaded and added open-source imagery to a project, created training samples using the Training Samples Manager pane, and exported them to a format compatible with a deep learning model for training.

Next, you'll create a deep learning model and identify all the trees on the plantation. Before you can begin to detect palm trees, you need to train a model. Training a model entails taking your training sample data and putting it through a neural network over and over again.

This computationally intensive process will be handled by a geoprocessing tool, but this is how the model will learn what a palm tree is and is not. Once you have a model, you'll apply it to your imagery to automatically identify trees. The Train Deep Learning Model geoprocessing tool uses the image chips you labeled to determine what combinations of pixels in a given image represent palm trees.

You'll use these training samples to train a single-shot detector SSD deep learning model. Depending on your computer's hardware, training the model can take more than an hour. It's recommended that your computer be equipped with a dedicated graphics processing unit GPU. If you do not want to train the model, a deep learning model has been provided to you in the project's Provided Results folder.

Optionally, you can skip ahead to the Palm tree detection section of this lesson. First, you'll set the tool to use your training samples. The imagechips folder contains two folders, two text files, a. Next, you'll set the number of epochs that your model will run. An epoch is a full cycle through the training dataset.

During each epoch, the training dataset you stored in the imagechips folder will be passed forward and backward through the neural network one time.

Next, you'll ensure that you are training the correct model type for detecting objects in imagery. The model type will determine the deep learning algorithm and neural network that you will use to train your model.

In this case, you're using the single-shot detector method because it's optimized for object detection. Next, you'll set the batch size. This parameter determines the number of training samples that will be trained at a time. Next, you'll ensure that the model runs for all epochs. Model arguments, the parameter values used to train the model, vary based on the model type you choose, and can be customized.

For more information about choosing model arguments, see the Train Deep Learning Model documentation. Otherwise, skip the next step.

If the model fails to run, reducing the Batch Size parameter can help. You may have to set this parameter to 4 or 2 and rerun the tool. However, this may reduce the quality of your trained model's results. The bulk of the work in extracting features from imagery is preparing the data, creating training samples, and training the model. Now that these steps have been completed, you'll use a trained model to detect palm trees throughout your imagery.

Object detection is a process that typically requires multiple tests to achieve the best results. There are several parameters that you can alter to allow your model to perform best. To test these parameters quickly, you'll try detecting trees in a small section of the image.

Once you're satisfied with the results, you'll extend the detection tools to the full image. If you did not train a model in the previous section, a deep learning package has been provided for you in the Provided Results folder. Classifying features is a GPU-intensive process and can take a while to complete depending your computer's hardware. If you choose to not detect the palm trees, results have been provided and you may skip ahead to the Refine detected features section.

First, you'll set the imagery from which you want to detect features. Next, you'll name the feature class of detected objects. Next, you'll choose the model you created to detect the palm trees. If you did not train a deep learning model, browse to the project's folder. Open Provided Results. Click OK. Next, you'll set some of the model's arguments. Arguments are used to adjust how the model runs for optimal results. When performing convolution of imagery in convolutional neural network modeling, you are essentially shrinking the data, and the pixels at the edge of the image are used much less during the analysis, compared to inner pixels.

The padding parameter adds an additional boundary of pixels to the outside edges of the image. This reduces the loss of information from the valid edge pixels and shrinking.

You'll leave this as the default. The threshold argument is the confidence thresholdпїЅhow much confidence is acceptable to label an object a palm tree? This number can be tweaked to achieve desired accuracy. This controls how much each feature is allowed to intersect. A lower number for this argument would specify that the objects could not overlap and are considered individual features.

Before running the tool, you'll set some environments. Next, you'll set a processing extent. This parameter forces the tool to only process the imagery that falls within the current map extent. Since the object detection process is hardware intensive, it is best to run the tool on a smaller area to test your parameters before running it on a full imagery dataset.

After you choose Current Display Extent , the coordinates of the extent's geographic bounding box are displayed. Observe your results. You can try experimenting with the arguments to see how this impacts your results. Once you have arguments that yield good results, you'll detect palm trees across the entire image.

Since the tool is running on the full imagery dataset, processing time will increase based on your computer's hardware. If you do not run the model to detect the palm trees, a dataset of palm trees has been provided. Browse to the Kolovai folder and to the Provided Results folder, open the Results geodatabase, and double-click the DetectedPalms feature class. When the tool finishes, observe your results. The color of your final results may differ from the image provided.

You'll notice that some of your palm trees have overlapping features. This means that many trees have been identified multiple times leading to an erroneous count of the total number of trees.

After you change the symbology to make this issue clearer, you'll remove these overlapping features with a geoprocessing tool. The Symbology pane appears. For Color , choose No color. For Outline color , choose Solar yellow. For Outline width , type 1. Observe your results again now that the symbology has been changed. Ensuring an accurate count of palm trees in important.

Since many trees have been counted multiple times, you'll use the Non Maximum Suppression tool to resolve this. However, you have to be careful; palm trees' canopies can overlap. So, you'll remove features that are clearly duplicates of the same tree while ensuring that separate trees with some overlap are not removed. First, you'll choose your layer of palm trees created by the model. If you skipped the previous section, a dataset of palm trees has been provided. Each palm tree in this dataset has a confidence score to represent how accurately the model identified each feature.

You'll enter this field into the tool. Each feature detected has also been marked with its appropriate class. Recall that this model had one class, Palm. This was recorded when you used the model. The Max Overlap Ratio determines how much overlap there can be between two features before they are considered the same feature. A higher value indicates that there can be more overlap between two features. The feature with the lower confidence will be removed. You'll set the tool to remove any trees with more than 50 percent overlap.

A new layer is added in the Contents pane. It has the same symbology as the DetectedPalms layer. You'll see that there are fewer trees with overlap in the new layer. You can rerun the tool as needed with different Max Overlap Ratio values to achieve optimal results. You've just trained and used a model to detect palm trees. Next, you'll use raster functions to obtain an estimate of vegetation health for each tree detected in your study area.

It is important to realize that your model's results might not be perfect the first time. Training and implementing a deep learning model is a process that can take several iterations to provide the best results.

Better results can be achieved by doing the following: Increasing your initial sample size of features Ensuring that your training samples are accurately capturing the features you want to detect Making sure your training samples include features of different sizes Adjusting the geoprocessing tools' parameters Retraining an existing model using the Train Deep Learning Model tool's advanced parameters.

In the previous module, you used a deep learning model to extract coconut palm trees from imagery. In this module, you'll use the same imagery to estimate vegetation health by calculating a vegetation health index. To assess vegetation health, you'll calculate the Visible Atmospherically Resistant Index VARI , which was developed as an indirect measure of leaf area index LAI and vegetation fraction VF using only reflectance values from the visible wavelength:.

Typically, you would use reflectance values in both the visible and the near infrared NIR wavelength bands to estimate vegetation health, as with the normalized difference vegetation index NDVI. However, the imagery you downloaded from OpenAerialMap is a multiband image with three bands, all in the visible electromagnetic spectrum, so you'll use the VARI instead.

Raster functions are quicker than geoprocessing tools because they don't create a new raster dataset. Instead, they perform real-time analysis on pixels as you pan and zoom. The Raster Functions pane appears. For Raster , choose the Imagery raster layer. The function requires you to provide the band index number that corresponds to the input bands for the formula. The input underneath the Band Indexes parameter shows Red Green Blue, so you'll provide the band index numbers that correspond with the Red, Green, and Blue bands, in that order.

Make sure to put a single space between each band. For Band Indexes , type 1 2 3. By zooming and panning around the area, you can see features such as the coastline, roads, buildings, and fields. Next, you'll change how the raster draws on the map to make the VARI symbology more clear. Having a raster layer showing VARI is helpful, but not necessarily actionable. To figure out which trees need attention, you want to know the average VARI for each individual tree.

To find the VARI value for each tree, you'll extract the underlying average VARI value and symbolize them to show which trees are healthy and which need maintenance. First, you'll convert the polygon features to points. You have a point feature class in the centroid of each detected polygon.

If you zoom in to various locations and use the Measure tool, you'll see that the palm trees have an average radius of roughly 3 meters. In the next step, you'll create a polygon layer with a 3-meter buffer around each point. The Measure tool is found on the ribbon, on the Map tab, in the Inquiry group.

For Distance , type 3 and choose Meters. You have a polygon feature class depicting the location and general shape of each palm tree canopy.