ArcGIS Pro Tutorial

Introduction

This lab will follow a tutorial from at arcGIS online here. The tutorial will teach us how to process UAS data and classify the surface images based on certain specifications

Methods

First click on the link and open the demo map provided in the tutorial. You will be directed to open the Calculate Surface imperviousness task.This will allow you to extract three bands to distinguish surfaces. They are displayed on the fly so no new dataset is created.
Follow the instruction 1-4 and click create new layer.

This will yield a map like figure 1

Figure 1

This creates an image that is easier to read. The vegetation shows up in the infared. This will make it easier to distinguish features in step two.

In this step we will segment the image.

First select Group Similar pixels into segments in the task bar.
Then complete steps 1-4 to create the new layer. This will create an image that looks like figure 2.

Figure 4

You will proceed to step 2 where you can review the segmentation before you move to the final step.This will complete lesson one.

Lesson 2
Open up the classify imagery task and open up arc map. In arcMap add both the Louisville Neighborhood and Segmented images. You will process classify this data in arcMap using the image classification toolbar. Next draw rectangles over a few houses in the north west corner.

Figure 3. Squares draw using image classification toolbar. 

Next select all of the class and merge them giving them the new class name grey roofs. You will do this with all the feature until you have a map that looks like figure 4. You can then save the classifications.

Figure 4

Next run the Train the Classifier task in ArcGISpro. And complete steps 1-5.

Next run the classify imagery tool following these steps yield a map like figure 5.

Figure 5

You will then run step two of the task to reclassify the data. This yields a map like figure 6.

Figure 6

In lesson 3 we will create Accuracy assessment points. You will use the create accuracy assessment points tool to get figure 7 with 100 accuracy points.

Figure 7

In the final project you will tabulate the area and clean up the attribute tables one the tables are joined. This will show you parcels of land that have the highest impervious surface. You will end with a map that looks like figure 8.

Figure 8

 Conclusion
This data produced a map that shows areas that have the highest area of impervious surfaces in darker colors. This happens to be areas of roads. This can help city planners determine storm water fees based on areas of lower impervious surfaces. If we have our own similarly accurate data we can go about the same process to classify the image using our own specifications.