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.
arXiv:2610.12676v1 Announce Type: new Abstract: Wood-image benchmarks can yield optimistic estimates when images from the same tree occur in both training and test sets. WoodBR-Cross contains 4,875 t...
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.
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: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.
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.
arXiv:2609.26549v1 Announce Type: new Abstract: Individual tree crown segmentation from aerial imagery underpins tree-level carbon accounting, biodiversity, and restoration monitoring at landscape sc...
arXiv:2609. 11916v1 Announce Type: new Abstract: Camera traps often run in the field on edge hardware with limited or no connectivity, making small, locally-deployable vision-language models (VLMs) -- not frontier-scale ones -- the practically relevant class to evaluate for species identification.
The paper presents a neutrosophic decomposition of a Random Forest + XGBoost + Logistic Regression ensemble for bearing fault detection, yielding four uncertainty indicators: top‑class evidence (T‑hat), best‑competitor evidence (F‑hat), predictive entropy (I1‑hat), and decision disagreement (I2‑hat). Evaluated on CWRU and JNU benchmarks under a leave‑one‑condition‑out protocol, the ensemble achieves perfect accuracy on most CWRU loads but performs poorly on JNU, where Logistic Regression outperforms the tree ensembles. Additional experiments show that fusing time‑ and frequency‑domain models and using Jensen‑Shannon divergence improves uncertainty estimation, and that envelope‑spectrum features can separate fault classes with 99.57 % accuracy on CWRU. "whyItMatters":"The study demonstrates that refined neutrosophic uncertainty indicators can reveal limitations of conventional confidence scores and guide more reliable fault detection in variable‑speed industrial settings."
arXiv:2510. 09458v2 Announce Type: replace-cross Abstract: Interest in forestry automation is growing alongside rapid advances in deep learning.
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.
arXiv:2610.13610v1 Announce Type: new Abstract: Open-set insect species recognition faces significant challenges when deployed in real-world biodiversity scenarios, particularly when encountering unk...
arXiv:2605. 20716v5 Announce Type: replace Abstract: Random forests construct each tree with a different, randomised representation of the feature space.