This repository contains a tutorial illustrating how to create a deep neural network model that accepts an aerial image as input and returns a land cover label (forested, water, etc.) for every pixel in the image. Microsoft's Cognitive Toolkit (CNTK) is used to train and evaluate the model on an Azure Geo AI Data Science Virtual Machine or an Azure Batch AI GPU cluster. The method shown here was developed in collaboration between the Chesapeake Conservancy, ESRI, and Microsoft Research as part of Microsoft's AI for Earth initiative.
We recommend budgeting two hours for a full walkthrough of this tutorial. The code, shell commands, trained models, and sample images provided here may prove helpful even if you prefer not to complete the walkthrough: we have provided explanations and direct links to these materials where possible.
The training and evaluation steps of this tutorial can be performed on either:
|Geo AI DSVM||Batch AI|
|Create a Geo AI Data Science VM||Create a Batch AI cluster|
|Train a model in a Jupyter notebook||Train a model on the Batch AI cluster|
|Evaluate the model using a Jupyter notebook||Evaluate the model using a GPU cluster|
|Deploy your model in ArcGIS Pro||Learn how to scale up training|
This tutorial will train a pixel-level land cover classifier for a single epoch: your model will produce results similar to bottom-left. By expanding the training dataset and increasing the number of training epochs, we achieved results like the example at bottom right. The trained model is accurate enough to detect some features, like the small pond at top-center, that were not correctly annotated in the ground-truth labels.
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