Satellite imagery can contain a wealth of information, from the number of buildings in a city to the type of crops being grown in fields across the world. But extracting this data from an image is more complicated than working with vector datasets. Historically, to extract the buildings, or swimming pools, or palm trees in an image you would have needed to manually digitize each feature, a process that could take weeks or years depending on the size of the image. But with improvements in computing power and new, accessible tools for deep learning in ArcGIS Pro, anyone can train a computer to do the work of identifying and extracting features from imagery.
At the highest level, deep learning, which is a type of machine learning, is a process where the user creates training samples, for example by drawing polygons over rooftops, and the computer model learns from these training samples and scans the rest of the image to identify similar features. This blog post will be the first in a three-part series diving deeper into the process, starting with the software and hardware requirements to run a deep learning model in ArcGIS Pro.
https://www.esri.com/arcgis-blog/products/arcgis-pro/imagery/deep-learning-with-arcgis-pro-tips-tric...
