The folder contains several subfolders, an ArcGIS Pro project file (.aprx), and an ArcGIS toolbox (.tbx). Before you explore the other data, you will open the project file.

The project contains a map of a neighborhood near Louisville, Kentucky. The map includes Louisville_Neighborhood.tif, a 6-inch resolution image, 4-band aerial photograph of the area, and the Parcels layer, a feature class of land parcels. The Louisville_Neighborhood.tif imagery comes from the U.S. National Agriculture Imagery Program (NAIP).

Next, you'll look extract specific spectral bands from the imagery.

The Raster Functions pane appears.

Raster functions apply an operation to a raster image on the fly, meaning that the original data is unchanged and no new dataset is created. The output takes the form of a layer that exists only in the project in which the raster function was run. You will use the Extract Bands function to create an image with only three bands to distinguish between impervious and pervious surfaces.

The Extract Bands function appears.

The bands you extract will include Near Infrared (Band 4), which emphasizes vegetation; Red (Band 1), which emphasizes human-made objects and vegetation; and Blue (Band 3), which emphasizes water bodies.

The Method parameter determines the type of keyword used to refer to bands when you enter the band combination. For this data, Band IDs (a single number for each band) are the simplest way to refer to each band.

The Missing Band Action parameter specifies what action occurs if a band listed for extraction is unavailable in the image. Best Match chooses the best available band to use.

The new layer, named Louisville Neighborhood Extracted Bands_Louisville_Neighborhood.tif, is added to the map. It displays only the extracted bands.

The yellow Parcels layer covers the imagery and can make some features difficult to see. You will not use the Parcels layer until later in the project, so you will turn it off for now.

The Louisville Neighborhood Extracted Bands layer shows the imagery with the band combination that you chose (4 1 3). Vegetation appears as red, roads appear as gray, and roofs appear as shades of gray or blue. By emphasizing the difference between natural and human-made surfaces, you can more easily classify them later.

The Image Classification Wizard pane appears.

The wizard's first page (indicated by the blue circle at the top of the wizard) contains several basic parameters that determine the type of classification to perform. These parameters affect which subsequent steps will appear in the wizard. You will use the supervised classification method. This method is based on user-defined training samples, which indicate what types of pixels or segments should be classified in what way. (An unsupervised classification, by contrast, relies on the software to decide classifications based on algorithms.)

The object based classification type uses a process called segmentation to group neighboring pixels based on the similarity of their spectral characteristics.

Next, you will choose the Classification Schema. The Classification Schema is a file that specifies the classes that will be used in the classification. A schema is saved in an Esri classification schema (.ecs) file, which uses JSON syntax. For this workflow, you'll modify the default schema, NLCD2011. This schema is based on land cover types used by the United States Geological Survey.

The next parameter determines the Output Location value, which is the workspace that stores all the outputs created in the wizard. These outputs include training data, segmented images, custom schemas, accuracy assessment information, intermediate outputs, and resulting classification results.

Under Optional, you won't enter anything for Segmented Image, Training Samples, or Reference Dataset because you don't have any of these elements created ahead of time.

The next page of the Image Classification Wizard focuses on segmentation.

The Spectral detail parameter sets the level of importance given to spectral differences between pixels on a scale of 1 to 20. A higher value means that pixels must be more similar to be grouped together, creating a higher number of segments. A lower value creates fewer segments. Because you want to distinguish between pervious and impervious surfaces (which generally have very different spectral signatures), you choose a lower value.

The Spatial detail parameter sets the level of importance given to the proximity between pixels on a scale of 1 to 20. A higher value means that pixels must be closer to each other to be grouped together, creating a higher number of segments. A lower value creates fewer segments that are more uniform throughout the image. You choose a low value because not all similar features in your imagery are clustered together. For example, houses and roads are not always close together and are located throughout the full image extent.

Unlike the other parameters, the Minimum segment size in pixels parameter is not on a scale of 1 to 20 and can take any value. Segments with fewer pixels than the value specified in this parameter will be merged into a neighboring segment. You don't want segments that are too small, but you also don't want to merge pervious and impervious segments into one segment. The default value is acceptable in this case.

The Show Segment Boundaries Only parameter determines whether the segments are displayed with black boundary lines. This is useful for distinguishing adjacent segments with similar colors but may make smaller segments more difficult to see. Some of the features in the image, such as the houses or driveways, are fairly small, so you leave this parameter unchecked.

A preview of the segmentation is added to the map. It is also added to the Contents pane with the name Preview_Segmented.

At first sight, the output layer does not appear to have been segmented the way you wanted. Features such as vegetation seem to have been grouped into many segments that blur together, especially on the left side of the image. Tiny segments that seem to encompass only a handful of pixels dot the area as well. However, this image is being generated on the fly, which means the processing will change based on the map extent. At full extent, the image is generalized to save time. You will zoom in to reduce the generalization, so you can better see what the segmentation looks like with the parameters you chose.

The preview segmentation runs again. With a smaller map extent, the segmentation more accurately reflects the parameters you used, with fewer segments and smoother outputs.

The value 20 is the number that will be attributed to all segments identified as impervious through the classification process. It is more of a numeric label and is not intended to be used in any calculations.

Next, you'll add a subclass for gray roof surfaces.

In this tutorial, you won't create other roof types. However, in a project with more diverse buildings represented in the imagery, you might consider creating a red roof land-use type, since their spectral characteristics are different from gray roofs.

Next, you'll create a training sample on the map using this class.

A row is added to the wizard for your new training sample.

When creating training samples, you want to cover a high number of pixels for each land-use type. For this tutorial, having about six samples for each land-use type will be enough, but for a real project, where the imagery covers a much larger extent, you might need significantly more samples.

You'll create more training samples to represent the roofs of the houses.

Every training sample that you make is added to the wizard. Although you have only drawn training samples on roofs, each training sample currently exists as its own class. You'll eventually want all gray roofs to be classified as the same value, so you will merge the training samples that you created into one class.

The training samples collapse into one class. You can continue to add more training samples for gray roofs and merge them into the Gray Roofs class. The optimum strategy is to gather samples throughout the entire image.

Next, you'll add more land-use types.

Shadows are not actual surfaces and cannot be either pervious or impervious. However, shadows are usually cast by tall objects such as houses or trees and are more likely to cover grass or bare earth, which are pervious surfaces.

You can also turn off and on the Preview_Segmented layer to see the Louisville Neighborhood Extracted Bands layer to make better sense of the landscape.

Your customized classification schema is saved in case you want to use it again.

For the next parameter, you can specify the maximum number of samples to use for defining each class. You want to use all your training samples, so you will change the maximum number of samples per class to 0. Changing the maximum to 0 is a trick to ensure all training samples are used.

Next, you'll train the classifier and display a preview.

The process may take a long time, as multiple processes are run. First, the image is segmented (previously, you only segmented the image on the fly, which is not permanent). Then, the classifier is trained and the classification performed. When the process finishes, a preview of the classification is displayed on the map.

Depending on your training samples, your classification preview should appear to be fairly accurate (the colors in the dataset correspond to the colors you chose for each training sample class). However, you may notice that some features were classified incorrectly. For instance, in the example image, the muddy pond south of the neighborhood was incorrectly classified as a gray roof, when it is actually water. Classification is not an exact science and rarely will every feature be classified perfectly. If you see only a few inaccuracies, you can correct them manually later in the wizard. If you see a large number of inaccuracies, you may need to create more training samples. Also, in this case, roads and driveways are both impervious, so it won't change the final classification into pervious and impervious land cover.

The next page is the Classify page. You will use this page to run the actual classification and save it in your geodatabase.

The process runs and the classified raster layer Classified_Louisville is added to the map. It looks similar to the preview.

The next page is the Merge Classes page. You will use this page to merge subclasses into their parent classes. Your raster currently has seven classes, each representing a type of land use. While these classes were essential for an accurate classification, you are only interested in whether each class is pervious or impervious. You will merge the subclasses into the Pervious and Impervious parent classes to create a raster with only two classes.

When you change the first class, a preview is added to the map. The preview shows what the reclassified image will look like. When you change all of the classes, the preview should only have two classes, representing pervious and impervious surfaces.

One inaccuracy that is present in the example image is that some bare earth patches south of the neighborhood were misclassified as impervious. These patches are not connected to any other impervious objects, so you can reclassify them with relative ease.

With this tool, you can draw a polygon on the map and reclassify everything within the polygon.

With these settings, any pixels in the polygon will be reclassified to pervious surfaces. Next, you'll reclassify the bare earth patches.

The bare earth patches are automatically reclassified as a pervious surface.

While you likely noticed other inaccuracies in your classification, for the purposes of this tutorial, you will not make any more edits.

The tool runs and the reclassified raster is added to the map.

The Geoprocessing pane appears.

This tool calculates the area of some classes (in this tutorial, pervious and impervious) within specified zones (in this tutorial, each parcel).

The zone field must be an attribute that identifies each zone uniquely. The Parcel ID field has a unique identification number for each feature, so you will leave the parameter unchanged.

The class field determines the field by which area will be determined. You want to know the area of each class in your reclassified raster (pervious and impervious), so the Class_name field is appropriate.

The final parameter, Processing cell size, determines the cell size for the area calculation. By default, the cell size is the same as the input raster layer Louisville_Impervious, which is half a foot (in this case). You'll leave this parameter unchanged.

The tool runs and the table is added to the Contents pane, in the Standalone Tables section. You'll take a look at the table that you created.

The table has a standard ObjectID field, as well as three other fields. The first is the Parcel_ID field from the Parcels layer, showing the unique identification number for each parcel. The next two are the class fields from the Louisville_Impervious raster layer. The Impervious field shows the area (in square feet) of impervious surfaces per parcel, while the Pervious field shows the area of pervious surfaces.

You now have the area of impervious surfaces per parcel, but only in a stand-alone table. Next, you'll join the stand-alone table to the Parcels layer so that the area information becomes available in the layer. You'll perform the join based on the Parcel ID field, which is common to the Parcel layer and the stand-alone table.

The Add Join window appears.

• For Input Table, confirm that Parcels is selected.
• For Input Join Field, choose Parcel ID.
• For Join Table, confirm the Impervious_Area table is selected.
• For Join Table Field, confirm Parcel_ID is selected.

• IMPERVIOUS
• PERVIOUS

The Fields view for the Parcels attribute table appears.

With the Fields view, you can add or delete fields, as well as rename them, change their aliases, or adjust other settings. You'll change the field aliases of the two area fields to be more informative.

The changes to the attribute table are saved.

The Symbology pane for the Parcels layer appears. Currently, the layer is symbolized with a single symbol, as a yellow outline. You will symbolize the layer so that parcels with high areas of impervious surfaces appear differently than those with low areas.

A series of parameters becomes available. First, you will change the field that determines the symbology.

The symbology on the layer changes automatically. However, there is little variety between the symbology of the parcels because of the low number of classes.

The layer symbology changes again.

To interpret this map correctly, remember that the symbology chosen indicates the total impervious area for each parcel, and not their percentage of impervious surface. The parcels with the largest area of impervious surfaces appear to correspond to roads. These parcels are very large and almost entirely impervious. The large parcel with the pond and lawn in the center is symbolized in orange indicating a somewhat high impervious surface. This is due to the fact that the impervious path around the pond is thin but long and accumulates a sizable area. Turning your attention to the smaller parcels, observe that there is a great variation among them, based on the size of the buildings, driveways, and terraces they contain. In the future, the owners could decrease the impervious surface in their parcel by replacing an impervious driveway or terrace with pervious ones or perhaps installing a green roof on their house.