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.
\nObject annotations were created using the Computer Vision Annotation Tool (CVAT), covering four classes:
\nCoarse Woody Debris (CWD)
\nTree Stumps (STUMP)
\nVegetation
\nMISCELLANEOUS (MISC) — used for ground sampling point markers
\nThis 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).
\nPlease see our GitHub repo for pre-processing this dataset (https://github.com/garrulus-project/sam_peft/)
\nPlease note that the original orthomosaic files (.tif) will be made available in a separate publication.
\nThis 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
\n\n\n\ud83d\udcd8 If you use this dataset in your work, please cite our paper:
\n
Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery
ICRA 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}
}
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