Hugging Face Trending Papers

Advancing Wood Identification in the Philippines: Utilizing the Xylorix Platform for Efficient AI Model Development and Deployment for Five Key Species

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Illegal logging and timber trade continue to pose significant challenges in the Philippines, where accurate wood species identification is essential for enforcement but limited by the need for specialised equipment and expertise. This study aims to evaluate whether AI models for macroscopic wood identification can be developed and deployed by wood scientists without programming expertise using the Xylorix platform, focusing on five Philippine hardwood species: Mangium (Acacia mangium Willd.

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arXiv AI
Sep 4

Tree species mapping in Denmark: A comparison of spectral-temporal features with geospatial foundation model embeddings

The study maps tree species across Denmark using National Forest Inventory plots and Earth Observation data, comparing manually engineered spectral‑temporal features (STF) from Sentinel‑1 and Sentinel‑2 with embeddings from the foundation models TESSERA and AlphaEarth. Random forest, XGBoost, and MLP classifiers were evaluated, with the STF‑based MLP achieving the highest macro F1 scores for pure and mixed stands. The best model was applied nationally to produce a 10 m resolution tree species map, achieving 79.9% area‑adjusted accuracy and released as an open‑access product.

By Alkiviadis Koukos, Spyros Kondylatos, Thomas Nord-Larsen, Lotte Nyborg, Christian T{\o}ttrup, Kenneth Grogan
Hugging Face Trending Papers
Sep 3

Tree species mapping in Denmark: A comparison of spectral-temporal features with geospatial foundation model embeddings

The study maps tree species across Denmark using National Forest Inventory plots and Earth Observation data, comparing manually engineered spectral‑temporal features (STF) with embeddings from foundation models TESSERA and AlphaEarth. Random forest, XGBoost, and MLP classifiers were evaluated, with the STF‑based MLP achieving the highest macro F1 scores (0.843 for pure stands, 0.653 for mixed stands). The best model was applied nationally to produce a 10 m resolution tree species map, achieving 79.9% area‑adjusted accuracy and released as an open‑access resource.

arXiv Machine Learning
Aug 13

Automated binary classification of hazelnut X-ray images: A deep-learning benchmark for quality assessment

arXiv:2608. 11759v1 Announce Type: cross Abstract: Non-destructive X-ray imaging can reveal internal hazelnut defects that are difficult to detect by external inspection alone; however, automated interpretation remains challenging because of subtle radiographic differences among classes, marked class imbalance, and limited annotated data.

By Giancarlo Sportelli, Nicola Belcari, Roberta Pace, Umberto Bernardo, Sharmin Sultana, Alessandra Toncelli, Matteo Giaccone
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)