Stefano Puliti
Research Scientist
Authors
Xinlian Liang Yinrui Wang Jun Pan Janne Heiskanen Ningning Wang Siyu Wu Ilja Vuorinne Jiaojiao Tian Jonas Troles Myriam Cloutier Stefano Puliti Aishwarya Chandrasekaran James Ball Xiangcheng Mi Guochun Shen Kun Song Guofan Shao Rasmus Astrup Yunsheng Wang Petri Pellikka Mi Wang Jianya GongAbstract
No abstract has been registered
Authors
L. Duncanson P. M. Montesano A. Neuenschwander A. Zarringhalam N. Thomas D. M. Minor M. A. Wulder J. C. White E. Guenther T. Feng V. Leitold S. Hancock J. Armston Stefano Puliti A. I. Mandel S. Shah C. Silva M. Purslow J. Bruening Johannes Breidenbach Erik Næsset Svetlana Saarela N. Hunka J. R. Kellner S. P. Healey D. Schepaschenko J. Wallerman C. S.R. Neigh N. Carvalhais R. DubayahAbstract
No abstract has been registered
Authors
Stefano Puliti Emily R. Lines Jana Müllerová Julian Frey Zoe Schindler Adrian Straker Matthew J. Allen Lukas Winiwarter Nataliia Rehush Hristina Hristova Brent Murray Kim Calders Nicholas Coops Bernhard Höfle Liam Irwin Samuli Junttila Martin Krůček Grzegorz Krok Kamil Král Shaun R. Levick Linda Luck Azim Missarov Martin Mokroš Harry J. F. Owen Krzysztof Stereńczak Timo P. Pitkänen Nicola Puletti Ninni Saarinen Chris Hopkinson Louise Terryn Chiara Torresan Enrico Tomelleri Hannah Weiser Rasmus AstrupAbstract
1. Proximally sensed laser scanning presents new opportunities for automated forest ecosystem data capture. However, a gap remains in deriving ecologically pertinent information, such as tree species, without additional ground data. Artificial intelligence approaches, particularly deep learning (DL), have shown promise towards automation. Progress has been limited by the lack of large, diverse, and, most importantly, openly available labelled single-tree point cloud datasets. This has hindered both (1) the robustness of the DL models across varying data types (platforms and sensors) and (2) the ability to effectively track progress, thereby slowing the convergence towards best practice for species classification. 2. To address the above limitations, we compiled the FOR-species20K benchmark dataset, consisting of individual tree point clouds captured using proximally sensed laser scanning data from terrestrial (TLS), mobile (MLS) and drone laser scanning (ULS). Compiled collaboratively, the dataset includes data collected in forests mainly across Europe, covering Mediterranean, temperate and boreal biogeographic regions. It includes scattered tree data from other continents, totaling over 20,000 trees of 33 species and covering a wide range of tree sizes and forms. Alongside the release of FOR-species20K, we benchmarked seven leading DL models for individual tree species classification, including both point cloud (PointNet++, MinkNet, MLP-Mixer, DGCNNs) and multi-view 2D-based methods (SimpleView, DetailView, YOLOv5). 3. 2D Image-based models had, on average, higher overall accuracy (0.77) than 3D point cloud-based models (0.72). Notably, the performance was consistently >0.8 across scanning platforms and sensors, offering versatility in deployment. The top-scoring model, DetailView, demonstrated robustness to training data imbalances and effectively generalized across tree sizes. 4. The FOR-species20K dataset represents an important asset for developing and benchmarking DL models for individual tree species classification using proximally sensed laser scanning data. As such, it serves as a crucial foundation for future efforts to classify accurately and map tree species at various scales using laser scanning technology, as it provides the complete code base, dataset, and an initial baseline representative of the current state-of-the-art of point cloud tree species classification methods.
Division of Forest and Forest Resources
PathFinder - Towards an Integrated Consistent European LULUCF Monitoring and Policy Pathway Assessment Framework
Division of Forest and Forest Resources
A Decision Support System for emerging forest management alternatives
This project aims to develop advanced tree growth models using LiDAR-derived, high-density point cloud data to improve the simulation of forest dynamics under close-to-nature silvicultural practices. By modeling tree-level growth in structurally complex and heterogeneous stands, these models will support more accurate, spatially explicit forest simulations and inform sustainable and diversified forest management decisions.
Division of Forest and Forest Resources
SFI SmartForest: Bringing Industry 4.0 to the Norwegian forest sector
SmartForest will position the Norwegian forest sector at the forefront of digitalization resulting in large efficiency gains in the forest sector, increased production, reduced environmental impacts, and significant climate benefits. SmartForest will result in a series of innovations and be the catalyst for an internationally competitive forest-tech sector in Norway. The fundamental components for achieving this are in place; a unified and committed forest sector, a leading R&D environment, and a series of progressive data and technology companies.