The paper introduces MS-ALS-SPECIES, an open multispectral airborne laser scanning dataset designed for tree species classification. It contains 6,326 segment-level point clouds of individual trees from nine species in southern Finland, collected with two multispectral laser systems—HeliALS and Optech Titan—providing high point densities. The authors also present a crowdsourcing application for efficient field reference data collection and demonstrate the dataset’s utility through new species‑classification analyses building on prior work.
By Matti Hyypp\"a, Klaara Salolahti, Eric Hyypp\"a, Xiaowei Yu, Josef Taher, Leena Matikainen, Matti Lehtom\"aki, Paula Litkey, Teemu Hakala, Harri Kaartinen, Juha Hyypp\"a, Antero Kukko
arXiv:2511.16084v4 Announce Type: replace-cross
Abstract: Hyperspectral image (HSI) classification typically involves large-scale data and computationally intensive training, which limits the practic...
By Meihua Zhou, Liping Yu, Xinyu Tong, Wai Kin Fung, Ruiguo Hu, Jiarui Zhao, Nan Wan
arXiv:2510. 09458v2 Announce Type: replace-cross Abstract: Interest in forestry automation is growing alongside rapid advances in deep learning.
By David-Alexandre Duclos, William Guimont-Martin, Gabriel Jeanson, Arthur Larochelle-Tremblay, Martine Lapointe, Th\'eo Defosse, Fr\'ed\'eric Moore, Philippe Nolet, Fran\c{c}ois Pomerleau, Philippe Gigu\`ere
Hyperspectral remote sensing provides dense spectral measurements that are indispensable for material-level Earth observation, yet the construction of a general-purpose hyperspectral foundation model...
HyperSAM is a promptable foundation model for hyperspectral remote sensing that integrates a data‑centric synthesis pipeline with a spectral adaptation architecture based on Segment Anything Model 3 (SAM3). The model generates full‑spectrum hyperspectral cubes from high‑resolution multispectral imagery using a physics‑informed abundance‑transfer generator, and employs SAM3‑derived pseudo‑masks for object‑centric supervision. With a frozen SAM3 RGB branch, a trainable hyperspectral encoder, ControlNet‑style feature injection, and a mixture‑of‑experts mask refiner, HyperSAM demonstrates strong generalization across diverse hyperspectral tasks such as classification, anomaly detection, change detection, target detection, and airborne oil‑spill mapping.
By Li Pang, Xinqiao Wu, Jing Yao, Pedram Ghamisi, Jun Zhou, Zhengchao Chen, Deyu Meng, Xiangyong Cao
The paper presents a framework that enhances deep‑learning tree‑cover mapping in New South Wales by fusing multiple imagery sources and normalizing image quality. It introduces an image‑composition technique that removes defects and a prediction‑fusion method that reduces reliance on any single image, together cutting errors by 38.2 % and 53.6 % respectively. Label transfer across diverse imagery further boosts data efficiency, yielding error reductions of 28.1 %–76.2 % and a 13‑fold decrease in performance variability across dates.
By Kal Backman, Jared Wood, Adam Roff
The study presents a new approach to detect Christmas tree plantations in high‑resolution aerial imagery, treating the task as a rare‑target semantic segmentation problem. It introduces a Hard Negative Mining strategy that significantly improves precision‑recall performance, achieving an IoU of 0.733 and an F1‑score of 0.846 on a 2020 test set. Temporal transfer experiments demonstrate the model’s ability to generalize across years, while large‑scale validation highlights the challenge posed by the plantations’ small spatial footprint.
By Francesca Razzano, Emanuele Dalsasso, Adrien Baysse-Lain\'e, Silvia Liberata Ullo, Gilda Schirinzi, Jocelyn Chanussot
The study evaluates recurrent neural networks (RNNs) for classifying tree species from Sentinel‑2 time‑series data, focusing on distinguishing pure from mixed Norway spruce pixels. RNNs outperform Random Forest and XGBoost, achieving a Macro‑F1 of 0.851. Integrated Gradients analysis identifies key spectral‑temporal patterns—especially after day of year 125 and in band B11—that drive class separation, offering insights into phenological periods useful for mapping mixed forest structures.
By Maximilian Kirsch, Christine Preisach
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: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) with embeddings from foundation models TESSERA and AlphaEarth. Random forest, XGBoost, and MLP classifiers were evaluated, with the STF‑based MLP achieving the highest macro F1 scores (0.843 for pure stands, 0.653 for 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 resource.
arXiv:2609.24787v1 Announce Type: new
Abstract: Forest inventories increasingly rely on artificial intelligence (AI) models to derive forest attributes from large-scale 3D point clouds. Current model...
By Yuanwen Yue, Stefano Puliti, Damien Robert, Atakan Topalo\u{g}lu, Binbin Xiang, Maciej Wielgosz, Jan Dirk Wegner, Rasmus Astrup, Christian Rupprecht, Konrad Schindler