Training Deep Learning Models with ArcGIS: A Simple Guide
Training Deep Learning Models with ArcGIS: A Simple Guide
Hey friend, ever wanted to train your own deep learning models using ArcGIS? It’s easier than you think! Let’s break down the process.
First, you need your training data. This isn’t just any data; it’s specifically formatted data exported from ArcGIS’s “Export Training Data for Deep Learning” tool. Think of it as a neatly organized package containing images, labels (telling the model what’s in the images), and a JSON file describing your model’s structure. You’ll point ArcGIS to this folder, which can live on your local file system, a network share, or in various cloud storage locations (like cloud raster stores).
Next, you need to tell ArcGIS where you want to save your finished, trained model. This trained model will be a handy .dlpk package. You can specify a file path (again, local, network, or cloud) or even just give it a name, and ArcGIS will add it as an item to your ArcGIS portal. Just make sure the chosen location is a registered data store on your ArcGIS server.
Finally, you choose the *type* of deep learning model you want to train. ArcGIS supports several types, including:
- Image Translation
- Object Classification
- Object Detection
- Object Tracking
- Pixel Classification
The specific options available will depend on the type of processing you are doing. It’s all pretty straightforward once you have your data ready.
And that’s it! You’ve successfully trained your deep learning model using ArcGIS. Pretty cool, right? Let me know if you have any questions – I’m happy to help!
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