A Python package for point cloud classification using color and curvature
pymccrgb is a Python package for multiscale curvature classification of point clouds with color attributes.
It extends a popular classification method (MCC lidar)  to point cloud datasets with multiple color channels, such as those commonly produced in surveys using drone photography or other platforms. It can be used to distinguish points from the ground surface and low vegetation in data produced by structure from motion photogrammetry, stereo photogrammetry, or multi-spectral lidar scanning, or to filter colorized lidar point clouds.
The intended users are scientists in geomorphology, ecology, or planetary science who want to classify point clouds for topographic analysis, canopy height measurements, or other spectral classification.
This package is developed for Linux and Python 3.6+. It depends on common Python packages like sklearn, numpy, the LibLAS C API, and MCC Python bindings.
You can install it with
conda env create -n pymcc conda activate pymcc conda install pymccrgb -c conda-forge
The LibLAS C library is required for MCC and
pymccrgb. The MCC wrapper also
requires Boost and the C++11 or later standard library. These are installed
with the conda package.
Example notebooks are available in the docs or at docs/source/examples.
Topography under tree cover
from pymccrgb import mcc, mcc_rgb from pymccrgb.datasets import load_mammoth_lidar from pymccrgb.plotting import plot_results # Load sample data (Mammoth Mountain, CA) data = load_mammoth_lidar(npoints=1e6) # MCC algorithm ground_mcc, labels_mcc = mcc(data) # MCC-RGB algorithm ground_mccrgb, labels_mccrgb = mcc_rgb(data) plot_results(data, labels_mcc, labels_mccrgb)
Results of MCC and MCC-RGB on a forested area near Mammoth Mountain, CA.
Read the documentation for example use cases, an API reference, and more at pymccrgb.readthedocs.io.
Bug reports are much appreciated. Please open an issue with the
and provide a minimal example illustrating the problem.
Feel free to suggest new features in an issue with the
If you would like to add a feature or fix a bug, please fork the repository, create a feature branch, and submit a PR and reference any relevant issues. There are nice guides to contributing with GitHub here and here. Please include tests where appropriate and check that the test suite passes (a Travis build or
pytest pymccrgb/tests) before submitting.
Support and questions
Please open an issue with your question.
 Evans, J. S., & Hudak, A. T. 2007. A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments. IEEE Transactions on Geoscience and Remote Sensing, 45(4), 1029-1038 doi
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