The paper introduces a biologically informed, semi‑supervised deep learning approach for hyperspectral tree species classification that fuses hyperspectral imaging and airborne laser scanning data with ecological knowledge. It uses a canopy graph and biologically inspired pseudo‑labelling, enhanced by expert priors derived from a large language model‑assisted, citation‑constrained workflow, to improve classification accuracy. Experiments on two real‑world forest datasets demonstrate macro‑F1 gains of 6.01 and 4.85 percentage points over leading classical HSI–ALS pipelines.
By Micha{\l} Romaszewski, Dominik Kope\'c, Micha{\l} Cholewa, Katarzyna Ko{\l}odziej, Przemys{\l}aw G{\l}omb, Jan Niedzielko, Jakub Charyton, Justyna Wylaz{\l}owska, Anna Jaroci\'nska
FOR-species20K is the largest public LiDAR benchmark for tree‑species classification, combining 25 datasets across 33 species. A multi‑resolution, multi‑view ConvNeXtV2 model raised the hidden‑test accuracy from 79.5 % to 85.0 %, placing first at submission. However, cross‑source experiments show that this score does not reflect true source‑disjoint generalization, with performance dropping dramatically when species are held out by source.
By Jae Joong Lee, Bedrich Benes
arXiv:2608.30149v1 Announce Type: new
Abstract: Individual tree measurements derived from Light Detection and Ranging (LiDAR) mounted on Unmanned Aerial Vehicles (UAV) provide valuable information fo...
By Katsuto Shimizu (Shikoku Research Center, Forestry and Forest Products Research Institute), Fumiaki Kitahara (Department of Forest Management, Forestry and Forest Products Research Institute), Tomohiro Nishizono (Department of Forest Management, Forestry and Forest Products Research Institute), Hideki Saito (Forestry and Forest Products Research Institute), Masayoshi Takahashi (Department of Forest Management, Forestry and Forest Products Research Institute), Shingo Obata (Hokkaido Research Center, Forestry and Forest Products Research Institute), Shunsuke Tei (Hokkaido Research Center, Forestry and Forest Products Research Institute), Naoyuki Furuya (Department of Forest Management, Forestry and Forest Products Research Institute), Tomoya Goto (Green Kogyo), Eiji Kodani (Department of Forest Management, Forestry and Forest Products Research Institute), Yusuke Yamada (Graduate School of Bioagricultural Sciences, Nagoya University)
arXiv:2606. 32023v1 Announce Type: cross Abstract: Forest attributes are essential for national-scale resource monitoring.
By Emilie Vautier, Cl\'ement Mallet, C\'edric Vega
The study maps tree species across Denmark using National Forest Inventory plots and Earth Observation data, comparing manually engineered spectral‑temporal features (STF) from Sentinel‑1 and Sentinel‑2 with embeddings from the foundation models TESSERA and AlphaEarth. Random forest, XGBoost, and MLP classifiers were evaluated, with the STF‑based MLP achieving the highest macro F1 scores for pure and mixed stands. The best model was applied nationally to produce a 10 m resolution tree species map, achieving 79.9% area‑adjusted accuracy and released as an open‑access product.
By Alkiviadis Koukos, Spyros Kondylatos, Thomas Nord-Larsen, Lotte Nyborg, Christian T{\o}ttrup, Kenneth Grogan
arXiv:2609.21903v1 Announce Type: new
Abstract: Leaf-wood segmentation of individual trees from LiDAR point clouds is essential for quantitative structure models (QSMs) used in non-destructive biomas...
By Roman Kaharlytskyi, Derek T. Robinson, Roberto Guglielmi