arXiv AI

FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data

arXiv:2606. 32023v1 Announce Type: cross Abstract: Forest attributes are essential for national-scale resource monitoring.

arXiv Machine Learning

Jul 31

Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States

arXiv:2607. 27217v1 Announce Type: cross Abstract: Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage, yet regional carbon monitoring remains constrained by the sparse spatial and temporal availability of field inventories and airborne structural measurements.

By Shashika Lamahewage, Chandi Witharana

rag multimodal safety

arXiv Machine Learning

Jun 19

Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision

arXiv:2606. 20291v1 Announce Type: new Abstract: Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes.

By Luke J. Zachmann, David D. Diaz, Vincent A. Landau, Chelsey Walden-Schreiner, Tony Chang, Nathan E. Rutenbeck, Katharyn A. Duffy, Kiarie Ndegwa, Andreas Gros, Scott Conway, Guy Bayes

computer-vision

arXiv Machine Learning

Aug 13

Transferable Above-Ground Biomass (AGB) Estimation Model from Multi-Sensor Data with Sparse Field Calibration

arXiv:2608. 11638v1 Announce Type: new Abstract: Spatially continuous quantification of forest above-ground biomass (AGB) is what makes carbon accounting credible and mitigation strategies actionable.

By Pann Thinzar Seint, Bryan Atwood, Subas Chhatkuli

fine-tuning safety

arXiv Computer Vision

Sep 21

The Role of Radiometric Features in Cross-Site Leaf-Wood Segmentation of LiDAR Point Clouds

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

computer-vision benchmarks

arXiv Computer Vision

Sep 1

SERA-H: Super-Resolution of Sentinel Time Series for Fine-Scale Canopy Height Mapping

arXiv:2512.18128v4 Announce Type: replace Abstract: High-resolution mapping of canopy height is essential for forest management and biodiversity monitoring. Although recent studies have led to the ad...

By Thomas Boudras, Martin Schwartz, Rasmus Fensholt, Martin Brandt, Ibrahim Fayad, Jean-Pierre Wigneron, Gabriel Belouze, Fajwel Fogel, Philippe Ciais

benchmarks

arXiv Computer Vision

Sep 22

Toward a foundation model for forest point clouds

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

computer-vision benchmarks

arXiv Machine Learning

Sep 24

Geospatial embeddings detect old-growth forests but buffered spatial validation narrows their advantage over Sentinel features

arXiv:2609.28194v1 Announce Type: new Abstract: Old-growth forests develop over centuries under minimal anthropogenic disturbance, producing structurally complex and biodiverse stands. In Europe, pro...

By Thomas Ratsakatika (Department of Geography, University of Cambridge, Cambridge, UK), Mihai Zotta (Fundatia Conservation Carpathia, Brasov, Romania), Srinivasan Keshav (Department of Computer Science and Technology, University of Cambridge, Cambridge, UK), Emily R. Lines (Department of Geography, University of Cambridge, Cambridge, UK)

rag

arXiv Computer Vision

Sep 1

CedarCypress3D: an annotated UAV-LiDAR dataset of individual trees in planted cedar and cypress forests

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)

computer-vision

arXiv Computer Vision

Sep 3

Multispectral airborne laser scanning dataset for tree species classification: MS-ALS-SPECIES

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

benchmarks

arXiv AI

Jul 14

Uncertainty Quantification for EO Regression Tasks: Building Height, Tree Canopy Height and Above-ground Biomass Estimation

arXiv:2607. 11412v1 Announce Type: cross Abstract: Earth Observation regression tasks such as building height, canopy height, and above-ground biomass estimation underpin critical applications in urban planning, forest monitoring, and climate policy, where both accuracy and reliability are critical.

By Ritu Yadav, Andrea Nascetti, Yifang Ban

benchmarks

arXiv Machine Learning

Jun 26

Self-Supervised Tree-level Biomass Estimation in Urban Environments From Airborne LiDAR and Optical Observations

arXiv:2606. 26194v1 Announce Type: cross Abstract: Urban tree biomass remains less spatially explicitly quantified than biomass in managed forests because many estimates rely on inventories or coarse products that cannot resolve individual crowns or fine-scale heterogeneity.

By Jose Bermudez (McMaster University, Hamilton, Ontario, Canada), Zilong Zhong (McMaster University, Hamilton, Ontario, Canada), Dominic Cyr (, Environment and Climate Change Canada, Montreal, Quebec, Canada), Camile Sothe (Planet Labs PBC, San Francisco, California, USA), Alemu Gonsamo (McMaster University, Hamilton, Ontario, Canada)

computer-vision

arXiv AI

Jul 7

SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests

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

computer-vision benchmarks

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