Garrulus Field-D Semantic Segmentation Dataset

The Garrulus Field-D dataset represents a 0.3-hectare post-harvest area located in the Arnsberg Forest, Germany. Data was acquired using an Unmanned Aerial Vehicle (UAV), and the area was reconstructed into a geo-referenced RGB orthomosaic with a spatial resolution of approximately 10 cm per pixel. Object annotations were created using the Computer Vision Annotation Tool (CVAT), covering four classes: Coarse Woody Debris (CWD) Tree Stumps (STUMP) Vegetation MISCELLANEOUS (MISC) — used for ground sampling point markers This particular dataset contains the pre-processed tensor files (for both training and testing), which were generated from the RGB orthomosaic using our custom tool, the Garrulus Dataset Library (GDL). Please see our GitHub repo for pre-processing this dataset (https://github.com/garrulus-project/sam_peft/) Please note that the original orthomosaic files (.tif) will be made available in a separate publication. This dataset is published alongside our paper that was accepted at the ICRA 2025 Workshop on Novel Approaches for Precision Agriculture and Forestry with Autonomous Robots 📘 If you use this dataset in your work, please cite our paper:Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV ImageryICRA 2025, and available on arXiv: https://arxiv.org/abs/2505.08932@misc{wasil2025peftsam, title = {{Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery}}, author = {Mohammad Wasil and Ahmad Drak and Brennan Penfold and Ludovico Scarton and Maximilian Johenneken and Alexander Asteroth and Sebastian Houben}, year = {2025}, eprint = {2505.08932}, archivePrefix = {arXiv}, primaryClass = {cs.RO}, url = {https://arxiv.org/abs/2505.08932}, note = {Accepted to the Novel Approaches for Precision Agriculture and Forestry with Autonomous Robots, IEEE ICRA Workshop 2025}}Garrulus Field-D数据集为一处位于德国阿恩斯贝格森林的0.3公顷收获后区域。本数据集通过无人机（Unmanned Aerial Vehicle, UAV）采集数据，研究区域被重建为带地理参考的RGB正射影像图，空间分辨率约为每像素10厘米。 目标标注通过计算机视觉标注工具（Computer Vision Annotation Tool, CVAT）完成，涵盖四大类别： 1. 粗木质残体（Coarse Woody Debris, CWD） 2. 树桩（Tree Stumps, STUMP） 3. 植被 4. 杂项（MISCELLANEOUS, MISC）——用于地面采样点标记 本数据集包含预处理后的张量文件（支持训练与测试），这些张量文件由RGB正射影像通过我们自研的Garrulus数据集库（Garrulus Dataset Library, GDL）生成。 有关本数据集的预处理流程，请参阅我们的GitHub仓库：https://github.com/garrulus-project/sam_peft/ 请注意，原始正射影像文件（.tif格式）将在另一篇学术出版物中公开。 本数据集与我们被接收于ICRA 2025自动化机器人精准农林业创新方法研讨会的论文同步发布。 📘 若您在研究工作中使用本数据集，请引用如下论文： 《面向无人机影像森林地面分割的视觉基础模型参数高效微调》（Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery），发表于ICRA 2025，可在arXiv平台获取：https://arxiv.org/abs/2505.08932 引用格式如下： @misc{wasil2025peft, title = {{Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery}}, author = {Mohammad Wasil and Ahmad Drak and Brennan Penfold and Ludovico Scarton and Maximilian Johenneken and Alexander Asteroth and Sebastian Houben}, year = {2025}, eprint = {2505.08932}, archivePrefix = {arXiv}, primaryClass = {cs.RO}, url = {https://arxiv.org/abs/2505.08932}, note = {Accepted to the Novel Approaches for Precision Agriculture and Forestry with Autonomous Robots, IEEE ICRA Workshop 2025} }