Datasets:
File size: 1,983 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
- name: image_series
dtype: string
- name: split
dtype: string
splits:
- name: train
num_bytes: 888005233
num_examples: 92
download_size: 888031817
dataset_size: 888005233
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-segmentation
size_categories:
- n<1K
---
# Alpine Grassland Root Segmentation
92 root images (2550x2196) with manual binary root/background masks from the 'Grassland' dataset, collected in alpine grasslands during a study on root growth and soil function. It is one of the nine root imaging datasets in a benchmark of 21 Transformer and ConvNet architectures for root segmentation. Images keep their image series (BidmerN, BidmerS, Phytotron2021) and the record's U-Net training/validation split. The dataset contains 92 images with pixel-level mask annotations.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
The original train/test/val split has been preserved in the `split` column.
## Citation
```bibtex
@article{smith2026systematic,
title={A systematic comparison of transformers and ConvNets for root segmentation across nine datasets},
author={Smith, Abraham George and Lamprinidis, Sotiris and Seethepalli, Anand and York, Larry M. and Han, Eusun and Möhl, Patrick and Boulata, Kyriaki and Thorup-Kristensen, Kristian and Petersen, Jens},
journal={Plant Methods},
volume={22},
pages={52},
year={2026},
publisher={BioMed Central}
}
```
The dataset itself can be cited as:
Möhl, P., von Büren, R. S., & Hiltbrunner, E. (2025). *Growth of alpine grassland will start and stop earlier under climate warming (dataset)* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.20440497.V2
*This dataset was reformatted from its original format to match HuggingFace standards.*
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