Deep learning based 3D point cloud regression for estimating forest biomass

@article{Oehmcke2021DeepLB,
  title={Deep learning based 3D point cloud regression for estimating forest biomass},
  author={Stefan Oehmcke and Leiyuan Li and Jaime Caballer Revenga and Thomas Nord‐Larsen and Katerina Trepekli and Fabian Gieseke and C. Igel},
  journal={Proceedings of the 30th International Conference on Advances in Geographic Information Systems},
  year={2021}
}
  • Stefan OehmckeLeiyuan Li C. Igel
  • Published 21 December 2021
  • Environmental Science
  • Proceedings of the 30th International Conference on Advances in Geographic Information Systems
Knowledge of forest biomass stocks and their development is important for implementing effective climate change mitigation measures. Remote sensing using airborne LiDAR can be used to measure vegetation structure at large scale. We present deep learning systems for predicting wood volume, above-ground biomass (AGB), and subsequently above-ground carbon stocks directly from airborne LiDAR point clouds. Specifically, we devise different neural network architectures for point cloud regression and… 

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