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Regularize Adjacent Building Footprint


Regularizes building footprints that have common boundaries.


  • 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 boundacry 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.

  • When regularizing building footprints that are derived from raster data, the regularization tolerance should be larger than the resolution of the source raster.

  • Features with adjacent boundaries must have a common attribute value in order to create a regularized boundary that does not overlap another feature. If no common attribute is available, consider the following steps:

    1. Use the Buffer tool with a buffer distance that matches the desired regularization tolerance.
    2. Process the buffered polygon features with the Union tool to create a single feature for the overlapping polygons.
    3. Use the Spatial Join tool to add the unique ID from the unioned polygons to the original input features that will be regularized.
    4. Run the Regularize Adjacent Building Footprint tool with the field containing the unique ID specified in the Grouping Field (group parameter in Python).
  • Consider using the Any Angles method for input features comprised of acute or reflex interior angles, or when the angular bend between two segments does not fall on a 45° interval between 90° and 180°.


RegularizeAdjacentBuildingFootprint_3d (in_features, group, out_feature_class, method, tolerance, precision, angular_limit)
ParameterExplanationData Type

The input features to process.

Feature Layer

The field that will be used to determine which features share coincident, non-overlapping boundaries.


The feature class that will be produced by this tool.

Feature Class

The method that will be used to regularize the input features.

  • RIGHT_ANGLESIdentifies the best line segments that fit the input feature vertices along 90° and 180° angles.
  • RIGHT_ANGLES_AND_DIAGONALSIdentifies the best line segments that fit the input feature vertices along 90°, 135°, and 180° interior angles.
  • ANY_ANGLESIdentifies the best fit line that falls along any angle while reducing the overall vertex count of the input features.

The maximum distance that the regularized footprint can deviate from the boundary of its originating feature.

Linear Unit

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


The maximum deviation of the best fit line's interior angles that will be tolerated when using the Right Angles and Diagonals (RIGHT_ANGLES_AND_DIAGONALS) method. This value should generally be kept to less than 5° to obtain best results. This parameter is disabled for other regularization methods.


Code sample

RegularizeAdjacentBuildingFootprint example 1 (Python window)

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

arcpy.env.workspace = 'c:/data'
arcpy.ddd.RegularizeAdjacentBuildingFootprint('rough_footprints.shp', 'Block_ID',
                                              '2 Meters', 0.10)
RegularizeAdjacentBuildingFootprint 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)

    desc = arcpy.Describe(lasd)
    if desc.spatialReference.linearUnitName in ['Foot_US', 'Foot']:
        unit = 'Feet'
        unit = 'Meters'
    ptSpacing = desc.pointSpacing * 2.25
    sampling = '{0} {1}'.format(ptSpacing, unit)
    # Classify overlap points
    arcpy.ddd.ClassifyLASOverlap(lasd, sampling)
    # Classify ground points
    # Filter for ground points, 'ground', class_code=[2])
    # Generate DEM
    arcpy.conversion.LasDatasetToRaster('ground', dem, 'ELEVATION', 
                                        'BINNING NEAREST NATURAL_NEIGHBOR', 
    # 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], 
    # Filter LAS dataset for building points
    lasd_layer = 'building points', lasd_layer, class_code=[6])
    # Export raster from lidar using only building points
    temp_raster = 'in_memory/bldg_raster', 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, 

except arcpy.ExecuteError:

Licensing information

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

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