arXiv Computer Vision August 31, 2026 By M\'elisande Teng, Arthur Ouaknine, Etienne Lalibert\'e, Yoshua Bengio, David Rolnick, Hugo Larochelle

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery

Read the original on arXiv Computer Vision →

The paper introduces BalSAM, a model that combines the Segment Anything Model (SAM) with Digital Surface Model (DSM) elevation data to improve tree crown instance segmentation from high‑resolution drone imagery. Experiments across boreal plantations, temperate forests, and tropical forests show that while off‑the‑shelf SAM does not beat a custom Mask R-CNN, fine‑tuning SAM end‑to‑end and incorporating DSM information yield promising results, especially for plantation sites.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv 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

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

Estimating Individual Tree Height and Species from UAV Imagery

arXiv:2603. 23669v2 Announce Type: replace-cross Abstract: Accurate estimation of forest biomass, a major carbon sink, relies heavily on tree-level traits such as height and species.

By Jannik Endres, Etienne Lalibert\'e, David Rolnick, Arthur Ouaknine

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

Hugging Face Trending Papers

Jun 28

Miti360: A Comprehensive Dataset for Improved Reforestation Monitoring

Over the past decade, interest in applying machine learning (ML) to automate forest monitoring has grown significantly. However, existing training datasets are predominantly drawn from North America, Europe, Asia, and Australia, leaving a critical gap in African forestry data.

fine-tuning

arXiv Computer Vision

Aug 28

Detection of Christmas tree plantations from high-resolution aerial imagery. A case study in the French Morvan

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

computer-vision