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Regularize Building Footprint

Summary

Normalizes the footprint of building polygons by eliminating undesirable artifacts in their geometry.

Illustration

Regularized building footprints

Usage

  • This tool utilizes a polyline compression algorithm to correct distortions in building footprint polygons created through feature extraction workflows that may produce undesirable artifacts.

  • The tolerance value defines the region surrounding the polygon's boundary that the regularized polygon must fit into. This region can best be visualized by converting the polygon's boundary to a line feature, and then buffering the line by the desired tolerance distance.

  • If your building footprints contain circular structures, those features should be processed first. A compactness ratio can be used to identify circular buildings. To calculate this value, do the following:

    1. Add a field of type double.
    2. Use the field calculator to compute the following formula:
      (4 * 3.14159265358979 * !shape.area!) / !shape.length! ^ 2
    3. A perfect circle will have a value of 1, but since the polygons typically processed with this tool have some irregularity, values closer to 1 are more likely to have a circular shape. Evaluate your results to identify the minimum value of a circular building and select values greater than or equal to this value prior to executing this tool with the CIRCLE method.
  • When the specified parameters cannot produce a regularized solution for a given input, the original feature is copied to the output. The value specified in the STATUS field will indicate whether the feature was regularized or not:

    • 0—Regularized feature
    • 1—Original feature
    Note:

    If your output contains features that could not be regularized, consider iteratively running the tool by selecting the unprocessed features and altering the parameters to identify a solution. Background imagery can be very helpful for evaluating the accuracy of the regularized output.

Syntax

RegularizeBuildingFootprint_3d (in_features, out_feature_class, method, tolerance, densification, precision, diagonal_penalty, min_radius, max_radius)
ParameterExplanationData Type
in_features

The polygons that represent the building footprints to be regularized.

Feature Layer
out_feature_class

The feature class that will be produced by this tool.

Feature Class
method

The regularization method to be used in processing the input features.

  • RIGHT_ANGLESUseful for building footprints that are primarily defined by right angles
  • RIGHT_ANGLES_AND_DIAGONALSUseful for buildings footprints that are comprised of right angles and diagonal sides
  • ANY_ANGLEUseful for buildings with highly irregular footprints
  • CIRCLEUseful for buildings with circular characteristics, like grain silos and water towers
String
tolerance

The maximum distance that the regularized footprint can deviate from the boundary of its originating feature. The specified value will be based on the linear units of the input feature's coordinate system.

Double
densification

The sampling interval that will be used to evaluate whether the regularized feature will be straight or bent. The densification must be equal to or less than the tolerance value.

This parameter is only used with methods that support right angle identification.

Double
precision

The precision used by the spatial grid employed in the regularization process. Valid values range from 0.05 to 0.25.

Double
diagonal_penalty

Controls the distance bias for creating right-angle connections. Distances smaller than the diagonal penalty will be used to create right angles.

This parameter is only used with the RIGHT_ANGLES_AND_DIAGONALS method.

Double
min_radius

The smallest radius that a regularized circle can have. A value of 0 implies there is no minimum size limit. This option is only available with the CIRCLE method.

Double
max_radius

The largest radius that a regularized circular can have. This option is only available with the Circle method.

Double

Code sample

RegularizeBuildingFootprint example 1 (Python window)

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

arcpy.env.workspace = 'c:/data'
arcpy.ddd.RegularizeBuildingFootprint('rough_footprints.shp', 
                                      'regularized_footprints.shp',
                                      method='Circle', tolerance=1.5, min_radius=10, 
                                      max_radius=20)
RegularizeBuildingFootprint example 2 (stand-alone script)

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

'''****************************************************************************
       Name: Classify Lidar & Extract Building Footprints
Description: Extract footprint from lidar points classified as buildings, 
             regularize its geometry, and calculate the building height.

****************************************************************************'''
import arcpy

lasd = arcpy.GetParameterAsText(0)
dem = arcpy.GetParameterAsText(1)
footprint = arcpy.GetParameterAsText(2)

try:
    desc = arcpy.Describe(lasd)
    if desc.spatialReference.linearUnitName in ['Foot_US', 'Foot']:
        unit = 'Feet'
    else:
        unit = 'Meters'
    ptSpacing = desc.pointSpacing * 2.25
    sampling = '{0} {1}'.format(ptSpacing, unit)
    # Classify overlap points
    arcpy.ddd.ClassifyLASOverlap(lasd, sampling)
    # Classify ground points
    arcpy.ddd.ClassifyLasGround(lasd)
    # Filter for ground points
    arcpy.management.MakeLasDatasetLayer(lasd, 'ground', class_code=[2])
    # Generate DEM
    arcpy.conversion.LasDatasetToRaster('ground', dem, 'ELEVATION', 
                                        'BINNING NEAREST NATURAL_NEIGHBOR', 
                                        sampling_type='CELLSIZE', 
                                        sampling_value=desc.pointSpacing)
    # Classify noise points
    arcpy.ddd.ClassifyLasNoise(lasd, method='ISOLATION', edit_las='CLASSIFY', 
                               withheld='WITHHELD', ground=dem, 
                               low_z='-2 feet', high_z='300 feet', 
                               max_neighbors=ptSpacing, step_width=ptSpacing, 
                               step_height='10 feet')
    # Classify buildings
    arcpy.ddd.ClassifyLasBuilding(lasd, '7.5 feet', '80 Square Feet')
    #Classify vegetation
    arcpy.ddd.ClassifyLasByHeight(lasd, 'GROUND', [8, 20, 55], 
                                  compute_stats='COMPUTE_STATS')
    # Filter LAS dataset for building points
    lasd_layer = 'building points'
    arcpy.management.MakeLasDatasetLayer(lasd, lasd_layer, class_code=[6])
    # Export raster from lidar using only building points
    temp_raster = 'in_memory/bldg_raster'
    arcpy.management.LasPointStatsAsRaster(lasd_layer, temp_raster,
                                           'PREDOMINANT_CLASS', 'CELLSIZE', 2.5)
    # Convert building raster to polygon
    temp_footprint = 'in_memory/footprint'
    arcpy.conversion.RasterToPolygon(temp_raster, temp_footprint)
    # Regularize building footprints
    arcpy.ddd.RegularizeBuildingFootprint(temp_footprint, footprint, 
                                          method='RIGHT_ANGLES')

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

Licensing information

  • ArcGIS Desktop Basic: Requires 3D Analyst
  • ArcGIS Desktop Standard: Requires 3D Analyst
  • ArcGIS Desktop Advanced: Requires 3D Analyst

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