Classify LAS Building (3D Analyst)—ArcGIS Pro

Summary

Classifies building rooftops and sides in LAS data.

Illustration

Classification of building rooftop points.

Usage

Points representing walls, vertical facades, and small rooftop features—such as dormers and chimneys—may not be included in the building classification. If such points are desired, consider running the tool with the options for classifying points that are above and below the roof.
The LAS data must have classified ground points before you can classify building rooftop points. Consider using the Classify LAS Ground tool if ground points have not been classified. The ground points must have a class code value of 2. If ground points have a class code value other than 2, use the Change LAS Class Codes tool to reassign the class code accordingly.
LAS points with class code values of 0, 1, and 6 will be evaluated to determine if they fit the characteristics of building rooftops. Points classified as buildings that do not meet this criteria will be reassigned to a class code value of 1 unless the option to reuse existing building classified points is specified.
The Method parameter is not used when the Is photogrammetric data option is specified.

Syntax

ClassifyLasBuilding(in_las_dataset, {min_height}, min_area, {compute_stats}, {extent}, boundary, {process_entire_files}, point_spacing, {reuse_building}, {photogrammetric_data}, {method}, {classify_above_roof}, {above_roof_height}, {above_roof_code}, {classify_below_roof}, {below_roof_code})

Parameter	Explanation	Data Type
in_las_dataset	The LAS dataset to be classified.	LAS Dataset Layer
min_height (Optional)	The height from the ground that defines the lowest point from which rooftop points will be identified.	Linear Unit
min_area	The smallest area of the building rooftop.	Areal Unit
compute_stats (Optional)	Specifies whether statistics should be computed for the LAS files referenced by the LAS dataset. Computing statistics provides a spatial index for each LAS file, which improves analysis and display performance. Statistics also enhance the filtering and symbology experience by limiting the display of LAS attributes, like classification codes and return information, to values that are present in the LAS file. COMPUTE_STATS —Statistics will be computed. NO_COMPUTE_STATS —Statistics will not be computed. This is the default.	Boolean
extent (Optional)	Specifies the extent of the data that will be evaluated by this tool.	Extent
boundary	A polygon feature that defines the area of interest to be processed by this tool.	Feature Layer
process_entire_files (Optional)	Specifies how the processing extent is applied. PROCESS_EXTENT —Only LAS points that intersect the area of interest will be processed. This is the default. PROCESS_ENTIRE_FILES —If any portion of a LAS file intersects the area of interest, all the points in that LAS file, including those outside the area of interest, will be processed.	Boolean
point_spacing	The average spacing of LAS points. This parameter is no longer used.	Linear Unit
reuse_building (Optional)	Specifies whether the existing building classified points will be reused or reevaluated. Specifies whether the existing building classified points will be reused or reevaluated. RECLASSIFY_BUILDING —Existing building classified points will be reevaluated to fit the criteria for plane detection, and points that do not fit the specified area and height will be assigned a value of 1. This is the default. REUSE_BUILDING —Existing building classified points will contribute to the plane detection process but will not be reclassified in the event they do not meet the criteria specified in the tool's execution. Use this option if the existing classification is desirable.	Boolean
photogrammetric_data (Optional)	Specifies whether the points in the LAS file were derived using a photogrammetric technique. Specifies whether the points in the LAS file were derived using a photogrammetric technique. NOT_PHOTOGRAMMETRIC_DATA —The points in the LAS file were obtained from a lidar survey, not from a photogrammetric technique for producing point clouds. This is the default. PHOTOGRAMMETRIC_DATA —The points in the LAS file were obtained using a photogrammetric technique for producing point clouds from overlapping imagery.	Boolean
method (Optional)	The classification method that will be used. AGGRESSIVE —Points that fit the planar rooftop characteristics with a relatively high tolerance for outliers will be detected. Use this method if the points are not well calibrated. STANDARD —Points that fit the planar rooftop characteristics with a relatively moderate tolerance for irregular points will be detected. This is the default CONSERVATIVE —Points that fit the planar rooftop characteristics with a relatively low tolerance for irregular points will be detected. Use this method if the building points are co-planar with points from objects that are not buildings.	String
classify_above_roof (Optional)	Specifies whether points above the planes detected for the roof will be classified. NO_CLASSIFY_ABOVE_ROOF —Points above the planes detected by this tool will not be classified. This is the default.. CLASSIFY_ABOVE_ROOF —Points above the planes detected by this tool will be classified.	Boolean
above_roof_height (Optional)	The maximum height of the points above the building rooftop that will be classified to the value designated in the Above Roof Class Code parameter.	Linear Unit
above_roof_code (Optional)	The class code that will be assigned to points above the roof.	Long
classify_below_roof (Optional)	Specifies whether points between the roof and the ground will be classified. NO_CLASSIFY_BELOW_ROOF —Points between the roof and the ground will not be classified. This is the default. CLASSIFY_BELOW_ROOF —Points between the roof and the ground will be classified.	Boolean
below_roof_code (Optional)	The class code that will be assigned to points between the ground and the roof.	Long

Derived Output

Name	Explanation	Data Type
derived_las_dataset	The LAS dataset that is classified for building rooftops.	LAS Dataset Layer

Code sample

ClassifyLasBuilding example 1 (Python window)

The following sample demonstrates the use of this tool in the Python window.

arcpy.env.workspace = 'C:/data'

arcpy.ClassifyLasBuilding_3d('Highland.lasd', minHeight='9 feet', 
                             minArea='30 Square Feet', compute_stats=True)

ClassifyLasBuilding example 2 (stand-alone script)

The following sample demonstrates the use of this tool in a stand-alone Python script.

'''****************************************************************************
       Name: Tile & Classify LAS Files
Description: Creates & classifies tiled LAS files.
****************************************************************************'''
# Import system modules
import arcpy
import tempfile
import math

in_las = arcpy.GetParameterAsText(1) # The LAS files that need to be tiled
out_folder = arcpy.GetParameterAsText(2) # folder for LAS files
basename = arcpy.GetParameterAsText(3) # basename for output files
out_lasd = arcpy.GetParameterAsText(4) # output LAS dataset


try:
    # Create temp LAS dataset to reference LAS files that will be tiled
    temp_lasd = arcpy.CreateUniqueName('temp.lasd', tempfile.gettempdir())
    arcpy.management.CreateLasDataset(in_las, temp_lasd)
    arcpy.ddd.TileLas(temp_lasd, out_folder, basename, out_lasd, las_version=1.4, 
                      point_format=7, file_size=300)
    arcpy.management.Delete(temp_lasd)
    arcpy.ddd.ClassifyLasGround(out_lasd, method='AGGRESSIVE')
    arcpy.ddd.ClassifyLasBuilding(out_lasd, min_height='3 Meters', min_area='4 Meters')
    arcpy.ddd.ClassifyLasByHeight(out_lasd, height_classification=[(3, 6), (4,20), (5,70)],
                                  noise='All Noise', compute_stats='COMPUTE_STATS')

except arcpy.ExecuteError:
    print(arcpy.GetMessages())

Environments

Extent, Parallel Processing Factor

Licensing information

Basic: Requires 3D Analyst
Standard: Requires 3D Analyst
Advanced: Requires 3D Analyst