Table of Contents
- Export training data for deep learning
- Model training
- Deployment and inference
- Visualize land cover classification on map
As an example, we use land cover classification to demonstrate of workflow of pixel-based image classification using
arcgis.learn. To begin with, we need input imagery as well as labels for each pixel. With the ArcGIS platform, these datasets are represented as layers, and are available in our GIS. This guide notebook showcases an end-to-end to land cover classification workflow using ArcGIS API for Python. The workflow consists of three major steps: (1) extract training data, (2) train a deep learning image segmentation model, (3) deploy the model for inference and create maps. To better illustrate this process, we will use NAIP imagery and high-resolution labeled data provided by the Chesapeake Conservancy land cover project.
from arcgis import GIS
gis = GIS("home")
To export training data, we need a labeled imagery layer that contains the class label for each location, and a raster input that contains all the original pixels and band information. In this land cover classification case, we will be using a subset of the one-meter resolution Kent county, Delaware, dataset as the labeled imagery layer and USA NAIP Imagery: Color Infrared as the raster input.
label_layer = gis.content.search("Kent_county_full_label_land_cover") # the index might change label_layer
m = gis.map("Kent county, Delaware") m