Dataset Card for WE3DS

This is a FiftyOne dataset with 2568 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
dataset = load_from_hub("Voxel51/WE3DS")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

WE3DS is an RGB-D image dataset for multi-class plant species semantic segmentation in crop farming. It contains 2,568 densely, hand-annotated RGB-D images (color image + stereo-derived distance map) captured under natural outdoor light conditions at the experimental farm of the University of Natural Resources and Life Sciences, Vienna (BOKU) in Groß-Enzersdorf, Austria, across 25 measurement dates in 2020 and 2021.

Each image is paired with a dense, per-pixel ground-truth segmentation mask covering soil, 7 crop species, and 10 weed species (18 "real" classes plus a void class for uncertain/unlabeled regions), along with a raw and a hole-filled ("refined") distance map, and per-image acquisition metadata (GPS location, timestamp, weather, wind, seeding date, and measured plant/canopy height).

• Curated by: Florian Kitzler, Norbert Barta, Reinhard W. Neugschwandtner, Andreas Gronauer, Viktoria Motsch (University of Natural Resources and Life Sciences, Vienna — BOKU)
• Funded by: The "DiLaAg — Digitalization and Innovation Laboratory in Agricultural Sciences" project, supported by the Government of Lower Austria and the private foundation Forum Morgen
• Shared by: Harpreet Sahota (FiftyOne-formatted version)
• Language(s): N/A (image dataset)
• License: CC BY 4.0

Dataset Sources

• Repository (original data + code): https://doi.org/10.5281/zenodo.7457983
• Paper: Kitzler, F.; Barta, N.; Neugschwandtner, R.W.; Gronauer, A.; Motsch, V. WE3DS: An RGB-D Image Dataset for Semantic Segmentation in Agriculture. Sensors 2023, 23, 2713. https://doi.org/10.3390/s23052713

Uses

Direct Use

• Training and benchmarking RGB, depth-only, or RGB-D semantic segmentation models for crop/weed discrimination in early growth stages.
• Studying the effect of fusing distance (depth) information with RGB imagery for outdoor agricultural scene understanding.
• Analyzing plant/weed distribution, canopy height, or acquisition-condition (weather, wind, date) effects on segmentation performance.

Out-of-Scope Use

• The dataset covers a single experimental farm site (Groß-Enzersdorf, Austria) and a fixed set of seeded crop/weed species in early growth stages only — it is not representative of other geographies, growth stages, or cropping systems, and models trained on it should not be assumed to generalize without further validation.

Dataset Structure

This is a flat image-type FiftyOne dataset (no grouping) with 2,568 samples and a train/test split carried over from the original release (1,540 train / 1,028 test, a ~60/40 split), stored as sample tags.

Field	FiftyOne type	Description
filepath	StringField	Path to the RGB image (images/img_XXXXX.png, 1600×1144, uint8)
tags	ListField	train or test, from the dataset's official split files
ground_truth	Segmentation	Dense per-pixel class-index mask (19 mask values: void + soil + 7 crops + 10 weeds); mask_targets stored on the dataset (see below)
depth_raw	Heatmap	Raw stereo-derived distance map (16-bit, same filename as the RGB image). 0 marks pixels where stereo matching failed due to occlusion or insufficient surface texture. Values are in units of 0.1 mm (e.g. a pixel value of 7268 ≈ 726.8 mm). range is set per-sample to [0, 99th percentile of nonzero pixels] to keep a handful of stereo-matching sentinel/noise pixels from washing out the visualized contrast
depth_refined	Heatmap	Hole-filled/interpolated version of depth_raw with no zero/invalid pixels. range is set per-sample to [min, max] of that image for full local contrast
location	GeoLocation	Capture GPS point ([lon, lat]). Unset (None) for 40 samples where the source GNSS reading was missing (see note below)
lat, lon	FloatField	Capture latitude/longitude (also duplicated into location above). Unset for the same 40 samples
date, time, utc	StringField	Local capture date/time and UTC timestamp, as recorded by the acquisition system
weather	StringField	Subjective weather condition at capture time: sunny, cloudy, or mixed
wind	StringField	Subjective wind intensity at capture time: light, medium, or strong
seeding_date	StringField	Date the parcel was seeded
height_mm	IntField	Measured plant/canopy height (mm) at time of capture

Label type notes:

• Segmentation masks are stored as fo.Segmentation rather than per-instance Detections/polygons, matching the source data (single-channel class-index PNGs). The original annotation process (CVAT, polygon-per-plant-instance) produced instance data, but the distributed masks are the rasterized, class-indexed result.
• Depth maps are stored as fo.Heatmap (dense per-pixel float/int maps) rather than as plain auxiliary image fields, so they render as color overlays in the FiftyOne App. Because the true depth range varies substantially per image (capture height was adjusted over the course of the field trials), a single fixed range across the whole dataset made the heatmaps look nearly uniform — ranges are therefore computed per sample as described in the field table above.
• The source info.csv has the literal string "nan" for lat/lon/utc on 40 samples (three short GNSS dropout windows during acquisition on 10.06.2021, img_01179–img_01208 and img_01229–img_01238). Rather than storing an invalid GeoLocation point (which breaks map-based visualization), these fields are left unset (None) on the affected samples.

dataset.info contents:

{
    "description": "WE3DS: an RGB-D image dataset for semantic segmentation of crops and weeds in agricultural fields (Kitzler et al., Sensors 2023).",
    "source": "https://doi.org/10.3390/s23052713",
    "class_names": ["void", "soil", "broad bean", "corn spurry", "red-root amaranth",
                    "common buckwheat", "pea", "red fingergrass", "common wild oat",
                    "cornflower", "corn cockle", "corn", "milk thistle", "rye brome",
                    "soybean", "sunflower", "narrow-leaved plantain",
                    "small-flower geranium", "sugar beet"],
}

dataset.mask_targets["ground_truth"] maps the 19 mask pixel values (0–18) to these same class names, in the same order (index 0 = void, 1 = soil, 2–18 = the 7 crop and 10 weed species).

Dataset Creation

Curation Rationale

Publicly available RGB image datasets for agricultural semantic segmentation were scarce and typically lacked additional distance (depth) information, despite evidence from other domains (indoor scenes, street scenes) that fusing RGB with depth improves segmentation quality. WE3DS was created to fill this gap: a real-world, natural-light, multi-class (not just soil/crop/weed) RGB-D dataset for crop farming, together with a benchmark showing that adding distance information does improve segmentation quality in this domain as well.

Source Data

Data Collection and Processing

Images were captured with a custom RGB-D sensor: two industrial XIMEA MC023CG-SY cameras (2.3MP Sony IMX174LLJ-C sensor, global shutter) in a stereo setup with a 4-5cm baseline and 12mm fixed-focal-length lenses (TAMRON M112FM12), achieving ~0.4mm/pixel ground resolution and ~1.6mm depth accuracy at a 90cm working height. The sensor was mounted top-down on a two-wheeled measurement trolley (60cm wheel spacing) alongside a GNSS module (Emlid Reach M2), and images were captured at 1 FPS while the trolley was pushed along crop rows.

Field trials ran over seven repetitions (four in 2020, three in 2021) across small parcels (2.5m × 9m in 2020, 1.5m × 5m in 2021) at the BOKU experimental farm in Groß-Enzersdorf, Austria, seeding 39 plant species across 247 parcels (a subset of which were usable after excluding parcels with excessive weeds, poor emergence, or bird damage). In total, 6,224 stereo image pairs were collected across 25 measurement dates and 84 parcels; 2,568 were selected (excluding poor-quality or plant-absent images) for ground-truth annotation and form this dataset. An additional 3,656 captured pairs were left unannotated at publication time.

For each selected pair, the raw stereo images were rectified using per-measurement-date camera calibration (HALCON map_image), then a distance map was computed via normalized cross-correlation stereo matching (binocular_distance, 21-pixel template). Non-overlapping border regions were cropped, producing the final 1600×1144 RGB image and aligned distance map used here. Stereo matching fails for occluded or texture-poor pixels (0.72% of pixels missing on average per image, up to 14% in the worst case) — these are the 0-valued pixels in depth_raw; depth_refined is the same map with those holes filled by interpolation.

Who are the source data producers?

Images and metadata were collected by the paper's authors and collaborators at BOKU's Institute of Agricultural Engineering and Institute of Agronomy, using their in-house acquisition software and hardware described above.

Annotations

Annotation process

Ground-truth annotation was performed in CVAT (Computer Vision Annotation Tool). Annotators drew labeled polygons around each individual plant instance (of a known, seeded species) in the unprocessed left RGB image. An additional class was used for uncertain cases and small/unknown weeds overlooked during manual weed control; together with species having too little annotation data, these were folded into the void class, which is excluded from the paper's benchmark training/evaluation. The resulting polygon annotations were then rectified and cropped to match the RGB-D image pair, producing the dense, per-pixel class-index masks distributed with this dataset. Across the dataset, 4,038 crop and 7,506 weed instances were annotated (average 4.5 plants/image: 1.6 crop + 2.9 weed instances, or ~38,234 plant pixels/image on average).

Who are the annotators?

Manual image annotation was performed by Nicole Burscha, Sarah Sierra Marroquin, and Melvin Lukas Polleichtner (per the paper's acknowledgments).

Personal and Sensitive Information

None. The dataset contains agricultural field imagery (soil, crops, weeds) and associated GPS/weather/timing metadata for a single non-residential experimental farm site; no personal or sensitive information is present.

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},
  doi       = {10.3390/s23052713}
}

APA:

Kitzler, F., Barta, N., Neugschwandtner, R. W., Gronauer, A., & Motsch, V. (2023). WE3DS: An RGB-D Image Dataset for Semantic Segmentation in Agriculture. Sensors, 23(5), 2713. https://doi.org/10.3390/s23052713

More Information

The original dataset and modified ESANet benchmark code are available at https://doi.org/10.5281/zenodo.7457983. In the source paper's benchmark, an RGB-D ESANet model trained at full input resolution (1280×960) reached 70.7% mIoU across 18 classes (soil + 17 plant species), compared to 70.1% for RGB-only and 48.5% for distance-only at the same resolution — confirming that the additional distance channel improves segmentation quality in this domain.

Dataset Card Authors

Harpreet Sahota (FiftyOne conversion and dataset card)