Las2Dod

The goal of this tool is to take 2 LAS point clouds and automatically compute a Dem of Difference while incorporating uncertainty testing through a cell-by-cell Welch's t-test.

Currently the tool is implemented through

• a python command line script
• a Standalone GUI Executable

Standalone GUI

A standalone executable was built from the source code @main_gui.py using tkinter and pyinstaller.

Until a release is built, The .exe can be downloaded along with two small testing LAS pointclouds here

The GUI had the advantage of not needing any additional software however it does not handle errors well. At this stage, the user interface is extremely basic.

Python Command line Script

Command Line Requirements

• Python >3 (tested on 3.7, 3.8)
• GDAL Python bindings
• Numpy
• Scipy
• Laspy

Setup

GDAL

Download GDAL wheel file matching python version and operating system here

With the desired python interpreter/environment

pip install "path_to_downloaded_wheel.whl"

Test in python with

from osgeo import gdal

Numpy, Scipy, Laspy

The rest of the required modules should work with a simple pip call

pip install scipy
pip install numpy
pip install laspy

Using main.py

Required Arguments

The only required parameters are beforeLAS, afterLAS, resolution in that order. beforeLAS, and afterLAS are both filepaths to LAS files.

resolution is the desired raster resolution, using the same units as the LAS files.

A simple example would be:

python main.py "path_to_before.las" "path_to_after.las" 0.01

Optional Arguments

--outputDir Pass this argument the path of a directory to control where the created files will be placed. By default, files will be created in the same directory as the script.

--pvalue This argument sets the p-value used to filter the raw DoD raster. Default is 0.05

--soilDensity Pass this argument a soil bulk density value in kg/m^3 to have the script estimate a mass of surface change from the changed volume. By default, a mass calculation will not take place. Assumes resolution is in meters.

--outputRasters Just including this argument will output the intermediate rasters used in the t-test and DoD calculations. By default, intermediate rasters will not be created.

--maskingRaster Pass this argument the filepath of a binary .tif file to clip the DoD output to that raster. (0's representing exclusion, 1's representing inclusion)

Outputs

Output File	Condition	What is the file?
filter_dod.tif	always	P-filtered DoD, not masked
raw_dod.tif	always	Raw DoD, not masked
output.csv	always	Statistics from p-filtered DoD operation
p_score.tif	always	Raster of calculated p-values
t_score.tif	always	Raster of calculated t-scores
filter_dod_c.tif	if --maskingRaster is used	P-filtered DoD, Masked
raw_dod_c.tif	if --maskingRaster is used	Raw DoD, Masked
a_mean.tif, b_mean.tif	if --outputRasters is used	Mean cell height values from the (b)efore and (a)fter LAS files
a_count.tif, b_count.tif	if --outputRasters is used	Count of points falling in each cell from the (b)efore and (a)fter LAS files
a_std.tif, b_std.tif	if --outputRasters is used	Standard Deviation of cell height values from the (b)efore and (a)fter LAS files

Assumptions and Limitations

• LAS files need to at least be roughly aligned and using the same coordiante system / units.
• A good deal of memory may be needed for large files. In testing, 2 LAS files of a combined size of 5.75 GB and 150 Million+ points used a little more than 14 GB of RAM
• Only takes .las and .tif files.