arXiv Machine Learning June 9, 2026 By Maciej Wielgosz, Stefano Puliti, Rasmus Astrup

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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 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 AI

Jul 9

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.

By Heeju Mun, Tackang Yang, Yunsoo Nam, Changhyun Choi

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arXiv AI

Jun 9

GVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence

arXiv:2606. 08014v1 Announce Type: cross Abstract: Accurate 3D instance segmentation in point cloud data is critical for machine vision applications.

By Liang Xu, Fangjing Wang, Jinyu Yang, Feng Zheng

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arXiv AI

Jun 18

A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation

arXiv:2605. 17131v2 Announce Type: replace-cross Abstract: Point cloud stands as the most widely adopted format for representing 3D shapes and scenes due to its simplicity and geometric fidelity.

By Minhas Kamal, Hiranya Garbha Kumar, Balakrishnan Prabhakaran

computer-vision benchmarks

arXiv Machine Learning

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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

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Related stories

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

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

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

GVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence

A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation

CrevasseSeg: A Label-Efficient UAV Crevasse Segmentation Framework