From Wood–Foliar Semantics to 3D Fuel Characterization: Transferable Deep Learning for Terrestrial LiDAR Across Global Forest Ecosystems

Nadeem Fareed, Carlos Alberto Silva, Alexander J. Gaskins, Susan J. Prichard, Andrew T. Hudak, Jinyi Xia, Cesar Ivan Alvites Diaz · Remote Sensing 2026 · 2026

DOI

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Topics

fuel-metrics, forest-segmentation

Feeds

P3 · Q1 journal

What they did

Introduces points2SBL, a geometry-driven deep learning framework for wood–foliar segmentation of terrestrial LiDAR that transfers across forest ecosystems and scanner platforms, and uses it for 3D fuel characterisation.

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BibTeX

@article{fareed2026-wood-foliar-fuel,
  title = {{From Wood–Foliar Semantics to 3D Fuel Characterization: Transferable Deep Learning for Terrestrial LiDAR Across Global Forest Ecosystems}},
  author = {Nadeem Fareed and Carlos Alberto Silva and Alexander J. Gaskins and Susan J. Prichard and Andrew T. Hudak and Jinyi Xia and Cesar Ivan Alvites Diaz},
  journal = {Remote Sensing 2026},
  year = {2026},
  doi = {10.3390/rs18183158}
}