Datasets:

synthgen
/

WE3DS-small

pretty_name: 'WE3DS-small: A 576-Image Subset of WE3DS'
license: cc-by-4.0
language:
  - en
tags:
  - image
  - semantic-segmentation
  - agriculture
  - weed-detection
  - crop-farming
  - computer-vision
  - rgb-d
  - plant-species
task_categories:
  - image-segmentation
task_ids:
  - semantic-segmentation
size_categories:
  - n<1K
source_datasets:
  - synthgen/WE3DS
dataset_info:
  features:
    - name: image
      dtype: image
    - name: annotation
      dtype: image
    - name: date
      dtype: string
    - name: time
      dtype: string
    - name: latitude
      dtype: float64
    - name: longitude
      dtype: float64
    - name: weather
      dtype: string
    - name: wind
      dtype: string
    - name: seeding_date
      dtype: string
    - name: height_mm
      dtype: int32
  splits:
    - name: train
      num_examples: 344
    - name: test
      num_examples: 232

WE3DS-small: A 576-Image Subset of WE3DS - https://zenodo.org/records/7457983

This is a curated subset of synthgen/WE3DS. All credit goes to the original authors.

Overview

A lightweight 576-image subset of the WE3DS dataset, designed for quick prototyping, demos, and experimentation without downloading the full 2,568-image dataset. Contains the same data fields and format as the full dataset.

Original Authors

Florian Kitzler, Norbert Barta, Reinhard W. Neugschwandtner, Andreas Gronauer, Viktoria Motsch

University of Natural Resources and Life Sciences, Vienna (BOKU)

Full dataset: synthgen/WE3DS
Original source: Zenodo (DOI: 10.5281/zenodo.7457983)
Paper: Sensors 2023, 23(5), 2713
License: CC-BY-4.0

Semantic Classes (19)

ID	Class	Type
0	void	background
1	soil	background
2	broad bean	crop
3	corn spurry	weed
4	red-root amaranth	weed
5	common buckwheat	crop
6	pea	crop
7	red fingergrass	weed
8	common wild oat	weed
9	cornflower	weed
10	corn cockle	weed
11	corn	crop
12	milk thistle	weed
13	rye brome	weed
14	soybean	crop
15	sunflower	crop
16	narrow-leaved plantain	weed
17	small-flower geranium	weed
18	sugar beet	crop

Dataset Structure

Data Fields

image: RGB image (PNG, 1600×1144)
annotation: Segmentation mask (PNG, 1600×1144, pixel values = class IDs 0–18)
date: Capture date (DD.MM.YYYY)
time: Local capture time (HH:MM:SS)
latitude: GPS latitude (WGS84)
longitude: GPS longitude (WGS84)
weather: Categorical — sunny, cloudy, or mixed
wind: Categorical — light, medium, or strong
seeding_date: Planting date (DD.MM.YYYY)
height_mm: Estimated plant height in millimeters

Data Splits

Split	Images
Train	344 (60%)
Test	232 (40%)

Usage

from datasets import load_dataset

dataset = load_dataset("synthgen/WE3DS-small")

# Access a training example
example = dataset["train"][0]
image = example["image"]           # PIL Image (RGB)
mask = example["annotation"]       # PIL Image (segmentation mask)

For the full dataset (2,568 images), use:

dataset = load_dataset("synthgen/WE3DS")

Citation

If you use this dataset, please cite the original authors:

@article{Kitzler2023WE3DS,
  author    = {Kitzler, Florian and Barta, Norbert and Neugschwandtner, Reinhard W. and Gronauer, Andreas and Motsch, Viktoria},
  title     = {WE3DS: An RGB-D Image Dataset for Semantic Segmentation in Agriculture},
  journal   = {Sensors},
  year      = {2023},
  volume    = {23},
  number    = {5},
  pages     = {2713},
  doi       = {10.3390/s23052713}
}

@dataset{Kitzler2023WE3DSdata,
  author    = {Kitzler, Florian and Barta, Norbert and Neugschwandtner, Reinhard W. and Gronauer, Andreas and Motsch, Viktoria},
  title     = {WE3DS: An RGB-D image dataset for semantic segmentation in agriculture},
  year      = {2023},
  publisher = {Zenodo},
  version   = {v1},
  doi       = {10.5281/zenodo.7457983},
  url       = {https://doi.org/10.5281/zenodo.7457983}
}

Acknowledgments

The original dataset was created as part of the "DiLaAg – Digitalization and Innovation Laboratory in Agricultural Sciences" project, funded by the Government of Lower Austria and the private foundation Forum Morgen.