# UruDendro4: A Benchmark Dataset for Automatic Tree-Ring Detection in Cross-Section Images of Pinus taeda L

arXiv: https://arxiv.org/abs/2511.20935 · HTML: https://mlsanity.com/paper/2511.20935/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections](https://mlsanity.com/paper/2212.03022/) (2022)
- [Automatic Wood Pith Detector: Local Orientation Estimation and Robust Accumulation](https://mlsanity.com/paper/2404.01952/) (2024)
- [DeepCS-TRD, a Deep Learning-based Cross-Section Tree Ring Detector](https://mlsanity.com/paper/2504.16242/) (2025)

## Built on by (most cited first, 1 of 1)

- [TRAS: An Interactive Software for Tracing Tree Ring Cross Sections](https://mlsanity.com/paper/2605.08025/) (2026)

## Abstract (arXiv)

Tree-ring growth represents the annual wood increment for a tree, and quantifying it allows researchers to assess which silvicultural practices are best suited for each species. Manual measurement of this growth is time-consuming and often imprecise, as it is typically performed along 4 to 8 radial directions on a cross-sectional disc. In recent years, automated algorithms and datasets have emerged to enhance accuracy and automate the delineation of annual rings in cross-sectional images. To address the scarcity of wood cross-section data, we introduce the UruDendro4 dataset, a collection of 102 image samples of Pinus taeda L., each manually annotated with annual growth rings. Unlike existing public datasets, UruDendro4 includes samples extracted at multiple heights along the stem, allowing for the volumetric modeling of annual growth using manually delineated rings. This dataset (images and annotations) allows the development of volumetric models for annual wood estimation based on cross-sectional imagery. Additionally, we provide a performance baseline for automatic ring detection on this dataset using state-of-the-art methods. The highest performance was achieved by the DeepCS-TRD method, with a mean Average Precision of 0.838, a mean Average Recall of 0.782, and an Adapted Rand Error score of 0.084. A series of ablation experiments were conducted to empirically validate the final parameter configuration. Furthermore, we empirically demonstrate that training a learning model including this dataset improves the model's generalization in the tree-ring detection task.