pymccrgb is a Python package for multiscale curvature classification of point clouds with color features. It extends a popular classification method (MCC lidar) [0] to point cloud datasets with multiple color channels, commonly produced in drone surveys. It can be used to extract points from the ground surface and vegetation in photogrammetric data for which multiple laser returns are not available.

It is intended for scientists in geomorphology, forest ecology, or planetary science who wish to classify points in datasets from structure from motion, stereo photogrammetry, or multi-spectral lidar.

It is best to use a virtual environment to install this package:

git clone https://github.com/rmsare/pymccrgb
cd pymccrgb
conda create -n pymcc -f environment.yml
conda activate pymcc

Read the documentation for example use cases, an API reference, and more. They are hosted at pymccrgb.readthedocs.io.

Bug reports are much appreciated. Please open an issue with the bug label, and provide a minimal example illustrating the problem.

Feel free to suggest new features in an issue with the new-feature label.

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.

Please open an issue with your question.

[0] 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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This work is licensed under the MIT License (see LICENSE). It also incorporates a wrapper for the mcc-lidar implementation, which is distributed under the Apache license (see pymcc/LICENSE.txt).