Summary
Regularizes building footprints that have common boundaries.
Usage
This tool uses a polyline compression algorithm to correct distortions in building footprint polygons created through feature extraction workflows that may produce undesirable artifacts.
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:
- Use the Buffer tool with a buffer distance that matches the desired regularization tolerance.
- Process the buffered polygon features with the Union tool to create a single feature for the overlapping polygons.
- Use the Spatial Join tool to add the unique ID from the unioned polygons to the original input features that will be regularized.
- 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°.
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 as follows:
- 0—Regularized feature
- 1—Original feature
Syntax
RegularizeAdjacentBuildingFootprint(in_features, group, out_feature_class, method, tolerance, precision, angular_limit)
| Parameter | Explanation | Data Type |
in_features | The input features to process. | Feature Layer |
group | The field that will be used to determine which features share coincident, non-overlapping boundaries. | Field |
out_feature_class | The feature class that will be produced by this tool. | Feature Class |
method | The method that will be used to regularize the input features.
| String |
tolerance | The maximum distance that the regularized footprint can deviate from the boundary of its originating feature. | Linear Unit |
precision | The precision of the spatial grid used in the regularization process. Valid values range from 0.05 to 0.25. | Double |
angular_limit | 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. | Double |
Code sample
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',
'regularized_footprints.shp',
'RIGHT_ANGLES_AND_DIAGONALS',
'2 Meters', 0.10)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
- Basic: Requires 3D Analyst
- Standard: Requires 3D Analyst
- Advanced: Requires 3D Analyst