Building Footrprint Extraction from Aerial LiDAR data
Project description
This Python package is BUilding Footprint Extractor Test
This of example of an how you can run your code
import LasBuildSeg as lasb
import numpy as np
# Define input parameters
input_laz = '<input>.laz' # Path to the input laz/las data file
epsg_code = <epsg_code> # EPSG code of the input laz data
multy = <DTM non-ground multipalction number> # Multiplication factor for DSM height enhancement
intermethod = '<interpolation method>' # Interpolation method ('cubic', 'nearest', or 'linear')
# Define default parameter values
constant = 3.6 # Adaptive threshold constant
block_size = 51 # Adaptive threshold block size
kernel_size = 3 # Morphological open kernel size
tri_threshold = 3 # Terrain Ruggedness Index threshold
# Define contour filtering parameters
min_size = 35
max_size = 5000
squareness_threshold = 0.3
width_threshold = 3
height_threshold = 3
CloseKernel_size = 15
# Generate DSM and DTM
lasb.generate_dsm(input_laz, epsg_code, intermethod)
lasb.generate_dtm(input_laz, epsg_code, intermethod, multy)
# Generate NDHM
lasb.generate_ndhm('dtm.tif', 'dsm.tif')
# Read NDHM image and profile
img, profile = lasb.read_geotiff('ndhm.tif')
# Transform DSM
lasb.DSM_transform('dsm.tif')
# Read transformed DSM and profile
dem, _ = lasb.read_geotiff('dsm3857.tif')
# Convert image to 8-bit
img_8bit = lasb.to_8bit(img)
# Apply adaptive thresholding
img_thresh = lasb.threshold(img_8bit, block_size, constant)
# Apply morphological opening
img_open = lasb.morph_open(img_thresh, kernel_size)
# Filter contours without TRI
building_mask=lasb.filter_contours(img_open, dem, profile, min_size, max_size, squareness_threshold, width_threshold, height_threshold, tri_threshold)
# Apply morphological closing
building_mask_closed = lasb.close(building_mask, CloseKernel_size)
# Write building mask to GeoTIFF
lasb.write_geotiff('buildings.tif', building_mask_closed, profile)
# Convert building mask to GeoJSON
lasb.building_footprints_to_geojson('buildings.tif', 'building.geojson')
# Print completion message
print('All steps are complete.')
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