Create a folder in your raster store to hold the exported training data. Make sure you have set up an image server before proceeding.
4️ Export the training data using ArcGIS Learn’s `export_training_data()` method, which will create a set of image chips and corresponding labels for each chip in your labeled imagery layer.
5️ Load the model architecture into memory by defining it as an object or loading it from disk using ArcGIS Learn’s `load_model()` function.
6️ Prepare the input data for classification by converting raster layers to NumPy arrays and reshaping them to fit the model’s input shape.
7️ Train the deep learning model using ArcGIS Learn’s `train()` function, which will optimize its weights based on a set of training data.
8️ Evaluate the performance of your trained model by calculating metrics such as accuracy and confusion matrix.
9️ Apply the trained model to an unseen image using ArcGIS Learn’s `predict()` function, which will classify each pixel based on its features.
10️ Visualize the results by adding a classified raster layer to your map and setting its symbology to display the land cover classes.
11️ Publish the trained model as an ArcGIS Image Server service, which will allow others to use it for classification tasks without having access to the original training data or model architecture.
References:
– Olaf Ronneberger, Philipp Fischer, Thomas Brox (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv preprint arXiv:1505.04597.
– Color difference. https://en.wikipedia.org/wiki/Color_difference.
– CVAT annotation tool. https://www.cvat.ai/.
– PNNL Parking Lot 1 and 2 and Pizza sequences. https://www.crcv.ucf.edu/data/ParkingLOT/.
– Most popular car color guess: Color. https://en.wikipedia.org/wiki/Most_popular_car_color_guess_(Color).
– GPT4RoI [42]. Table 3 shows the performance of our approach on the validation set, which is state-of-the-art in visual commonsense reasoning. Figure 4 illustrates how ViP-LLaVA can recognize a tiny region specified by the red contour.
In this context, we are using ArcGIS and deep learning to classify land cover data on maps. The steps involved include importing necessary libraries, retrieving labeled imagery data from an organization or portal URL, creating a folder for exported training data, exporting training data using `export_training_data()`, loading the model architecture into memory, preparing input data for classification, training the deep learning model using ArcGIS Learn’s `train()` function, evaluating performance metrics such as accuracy and confusion matrix, applying the trained model to an unseen image using `predict()` function, visualizing results by adding a classified raster layer to your map with symbology displaying land cover classes, and publishing the trained model as an ArcGIS Image Server service. References include U-Net for biomedical image segmentation, color difference calculations, CVAT annotation tool, PNNL Parking Lot 1 and 2 sequences, most popular car color guesses, GPT4RoI’s state-of-the-art performance in visual commonsense reasoning.