FuelDeep3D

Description

Provides tools for preprocessing, feature extraction, and segmentation of three-dimensional forest point clouds derived from terrestrial laser scanning. Functions support creating height-above-ground (HAG) metrics, tiling, and sampling point clouds, generating training datasets, applying trained models to new point clouds, and producing per-point fuel classes such as stems, branches, foliage, and surface fuels. These tools support workflows for forest structure analysis, wildfire behavior modeling, and fuel complexity assessment. Deep learning segmentation relies on the PointNeXt architecture described by Qian et al. (2022) <doi:10.48550/arXiv.2206.04670>, while ground classification utilizes the Cloth Simulation Filter algorithm by Zhang et al. (2016) <doi:10.3390/rs8060501>.

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installed size is  7.1Mb
  sub-directories of 1Mb or more:
    extdata   2.8Mb
    readme    4.0Mb

installed size is  7.1Mb
  sub-directories of 1Mb or more:
    extdata   2.8Mb
    readme    4.0Mb

installed size is  7.0Mb
  sub-directories of 1Mb or more:
    extdata   2.8Mb
    readme    4.0Mb

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installed size is  7.1Mb
  sub-directories of 1Mb or more:
    extdata   2.8Mb
    readme    4.0Mb

installed size is  7.1Mb
  sub-directories of 1Mb or more:
    extdata   2.8Mb
    readme    4.0Mb

installed size is  7.0Mb
  sub-directories of 1Mb or more:
    extdata   2.8Mb
    readme    4.0Mb

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