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Sparse tensor networks for individual tree diameter estimation in mixed forests — arXiv:2610.37784

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.37784
License (as archived): CC0-1.0
Archived: 2026-11-01T02:40:12.564Z
SHA-256 of the archived text: c93541fbc72bd8d1da4b3abcf07c899e09cfd966ec4f6822a050472cd7ed6e56
Stable URL of this page: https://metraipolis.com/documento/dc_df4a62bb765f

How to cite

Sparse tensor networks for individual tree diameter estimation in mixed forests — arXiv:2610.37784 — arXiv (en), https://arxiv.org/abs/2610.37784 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_df4a62bb765f (Metraipolis archive dc_df4a62bb765f, sha256 c93541fbc72bd8d1da4b3abcf07c899e09cfd966ec4f6822a050472cd7ed6e56)

BibTeX

@misc{dcdf4a62bb765f,
  title = {Sparse tensor networks for individual tree diameter estimation in mixed forests — arXiv:2610.37784},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.37784},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_df4a62bb765f; sha256 c93541fbc72bd8d1da4b3abcf07c899e09cfd966ec4f6822a050472cd7ed6e56},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.37784. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Measurements of tree diameter at breast height (DBH) are fundamental to forest inventories, but manual surveys across entire stands or regions are impractical. UAV-borne laser scanning (ULS) enables scalable automated forest surveys, yet canopy occlusion makes accurate DBH estimation from ULS point clouds non-trivial. Limited stem-level point density of ULS data constrains approaches relying on detailed stem reconstruction, such as Quantitative Structure Models (QSMs) and circle fitting. Empirical machine learning methods based on predefined point cloud metrics are an alternative. New deep learning approaches offer an even more principled solution, allowing models to learn directly from point clouds. Sparse tensor networks have shown particular promise, transforming forestry tasks such as tree species classification. We investigate whether these strengths can be extended to estimate individual tree DBH from ULS data across four mixed-species temperate forest plots in Germany. We further advance existing sparse tensor networks by introducing TreeNext, a lightweight deep learning architecture for direct individual tree parameter estimation from voxelized LiDAR point clouds. We present a fully automated forest inventory pipeline from individual tree instance segmentation to DBH regression, and quantify the impact of segmentation errors by comparing performance on automatically segmented and ground truth instances. TreeNext achieves an $R^2$ of 0.8355 on automatically segmented trees and 0.9380 on ground truth segments, outperforming strong tabular baselines including Random Forest and XGBoost. Segmentation quality emerges as the dominant error source in the automated pipeline. TreeNext proves highly data-efficient, performing well even in small data scenarios. Our findings establish sparse tensor networks as a practical foundation for automated individual tree DBH estimation from ULS.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.37784 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-01T02:16:43.852Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).