Tools to processing LiDAR point cloud based on CSF.
CSFTools provides a set of Python based tools, including:
- csfground.py: to filter a point cloud
- csfdem.py: a simple gridding and interpolation algorithm to generate a DEM/DSM/CHM
- csfnormalize.py: normalize point cloud
- csfclassify.py: use a scalar field to classify the point cloud into 2 classes
- csflai.py: compute leaf area index (LAI) from airborne discrete-return LiDAR data
- csfcrown.py: segment tree crowns from CHM
More details, please refer to User Manual.
This preprocessing tool requires a few python libraries, to make it easier to install, we recommend to use anaconda (python 3.6+), which has already been integrated with a few scientific computing libraries. Other libs:
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laspy: supporting reading and writing of las file. https://github.com/laspy/laspy run:
pip install laspy
or download the source and run:
python setup.py build python setup.py install
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GDAL
conda install gdal
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joblib: supporting parallel computing for python
pip install joblib
-
mahotas: computer vision library, supporting watershed transform, etc.
conda config --add channels conda-forge conda install mahotas
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CSF: ground filtering library, go to: https://github.com/jianboqi/CSF, and download all the source code: Under the folder python, run:
python setup.py build python setup.py install