Datasets:
File size: 2,097 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
- name: split
dtype: string
- name: date
dtype: string
- name: time
dtype: string
- name: lat
dtype: string
- name: lon
dtype: string
- name: utc
dtype: string
- name: weather
dtype: string
- name: wind
dtype: string
- name: seeding_date
dtype: string
- name: height_mm
dtype: string
- name: depth
dtype: image
- name: depth_refined
dtype: string
splits:
- name: train
num_bytes: 9013751981
num_examples: 2568
download_size: 8747620007
dataset_size: 9013751981
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-segmentation
size_categories:
- 1K<n<10K
---
# We3ds Weed Segmentation
This dataset provides real-world RGB imagery focused on weed segmentation within agricultural fields. It captures diverse weed and crop patterns under natural field conditions, offering a practical resource for developing and evaluating semantic segmentation models in precision agriculture applications. The dataset contains 2,568 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{kitzler2023we3ds,
title={WE3DS: An RGB-D image dataset for semantic segmentation in agriculture},
author={Kitzler, Florian and Barta, Norbert and Neugschwandtner, Reinhard W and Gronauer, Andreas and Motsch, Viktoria},
journal={Sensors},
volume={23},
number={5},
pages={2713},
year={2023},
publisher={MDPI}
}
```
Kitzler, F., Barta, N., Neugschwandtner, R. W., Gronauer, A., & Motsch, V. (2023). WE3DS: An RGB-D image dataset for semantic segmentation in agriculture [Dataset]. In Sensors (Vol. 23, Issue 5, p. 2713). Zenodo. https://doi.org/10.5281/zenodo.7457983
*This dataset was reformatted from its original format to match HuggingFace standards.*
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