arXiv AI

Self-Supervised Pretraining Improves Cross-Site and Cross-Scale Robustness of Point Cloud Leaf-Wood Segmentation

arXiv:2607. 06948v1 Announce Type: cross Abstract: The accuracy of existing leaf-wood segmentation methods for tree point clouds varies across forest types and sites.

arXiv Machine Learning

Jun 9

SegmentAnyTreeV2: Scaling Transformer-Based Tree Instance Segmentation Across Sensors, Platforms, and Forests

arXiv:2606. 08206v1 Announce Type: cross Abstract: We present SegmentAnyTreeV2, a sensor- and platform-agnostic framework for semantic and instance segmentation of forest point clouds.

By Maciej Wielgosz, Stefano Puliti, Rasmus Astrup

llms computer-vision benchmarks

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

arXiv AI

Jul 1

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.

By Emilie Vautier, Cl\'ement Mallet, C\'edric Vega

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 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 AI

Jul 7

Shifting from Discrete to Continuous Reference Data: QSM-Derived Horizontal Tree Biomass Distribution for Deep Learning Biomass Estimation

arXiv:2607. 05260v1 Announce Type: cross Abstract: Conventional modeling approaches for LiDAR-based above-ground biomass (AGB) estimation rely on discrete plot-level inventory aggregates.

By Nils Griese, Christoph Kleinn, Nils N\"olke

arXiv Machine Learning

Jun 15

NEST3D: A High-Resolution Multimodal Dataset of Sociable Weaver Tree Nests

arXiv:2606. 14562v1 Announce Type: cross Abstract: Sociable weaver nests function as complex ecological structures offering thermoregulatory microhabitats and sustaining diverse species; however, datasets used in prior studies lack fine-grained 3D structural detail.

By Constanza A. Molina Catricheo, Simon Boeder, Ting-Jia Guo, Giacomo May, Cl\'ement Berthelot, Devis Tuia, Friedrich Fedor Reinhard, Fabio Remondino, Benjamin Risse

llms computer-vision robotics multimodal benchmarks

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

2d ago

CrevasseSeg: A Label-Efficient UAV Crevasse Segmentation Framework

arXiv:2608. 15790v1 Announce Type: new Abstract: Crevasse mapping from uncrewed aerial vehicle (UAV) imagery matters for glaciological research and for field safety in glaciated terrain.

By Steven Wallace, William D Harcourt, Richard Hann, Aiden Durrant, Somayajulu Sripada, Georgios Leontidis

computer-vision fine-tuning benchmarks safety

Hugging Face Trending Papers

1d ago

Scale Matters: Adaptive Granularity Selection for Cross-Species 3D Plant Organ Segmentation

Recent 3D foundation models provide powerful feature representations for point cloud learning by controlling spatial granularity. However, relying on a fixed spatial granularity severely limits generalization in applications like plant phenotyping, where organ morphology and size vary substantially across species and growth stages.

computer-vision

arXiv Machine Learning

Aug 6

Above-ground Biomass Estimation with Geospatial Foundation Models

arXiv:2608. 04792v1 Announce Type: new Abstract: Accurate estimation of Above-Ground Biomass (AGB) from satellite imagery is essential for the large-scale monitoring of carbon stocks, yet it remains a challenging regression task at global scale.

By Ghjulia Sialellia, Linus Scheibenreif, Jan Dirk Wegner, Konrad Schindler

rag computer-vision benchmarks

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

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