Dataset Card for weeds-galore

This is a FiftyOne dataset with 156 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
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/weeds-galore")

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

Dataset Details

Dataset Description

WeedsGalore is a UAV-based, multispectral, multitemporal dataset for crop and weed segmentation in maize (Zea mays) fields, introduced in Celikkan et al., "WeedsGalore: A Multispectral and Multitemporal UAV-Based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields" (WACV 2025). All imagery comes from a single real (non- experimental) maize field in Marquardt, Potsdam, Germany, imaged with a DJI Phantom P4 Multispectral drone (R, G, B, red-edge, and near-infrared bands) at a ground sampling distance of 2.5 mm/px. Four flight campaigns were flown across the 2023 growing season (May 25, May 30, June 6, June 15), capturing progressively later growth stages and weed infestation levels. From these flights, 156 raw (non-orthorectified) 600x600 px tiles were sampled from 48 spatial locations and densely annotated with both semantic (6-class) and instance segmentation masks, averaging ~78 plant instances per tile — by far the highest instance density of any public weed-segmentation dataset at the time of publication.

This FiftyOne dataset parses the 156 annotated tiles (raw multiband images + semantic

• instance masks + official train/val/test splits) released by the authors. The four large, unlabeled orthomosaics mentioned in the source paper (one per acquisition date, useful for unsupervised/active-learning research) are not included in this parsed release, since only the 156 annotated per-tile samples were distributed with pixel-level labels.

• Curated by: Ekin Celikkan, Timo Kunzmann, Yertay Yeskaliyev, Sibylle Itzerott, Nadja Klein, Martin Herold (GFZ German Research Centre for Geosciences; Humboldt- Universität zu Berlin; Karlsruhe Institute of Technology)
• Funded by: Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS) and GFZ Potsdam; HPC resources funded by the Ministry of Science, Research and Culture of the State of Brandenburg (MWFK)
• Shared by: GFZ Data Services (original release); this FiftyOne-formatted version parsed and shared by Harpreet Sahota
• Language(s): N/A (image dataset; no natural language content)
• License: CC BY 4.0 (Attribution 4.0 International)

Dataset Sources

• Repository: github.com/GFZ/weedsgalore
• Paper: arxiv.org/abs/2502.13103
• Original data release: doidata.gfz.de/weedsgalore_e_celikkan_2024

Uses

Direct Use

• Training/evaluating semantic segmentation models for crop-vs-weed or fine-grained multi-species weed classification (background, maize, amaranth, barnyard grass, quickweed, weed_other) in maize fields.
• Training/evaluating instance segmentation models on dense, real-world plant scenes with heavy overlap and occlusion (~78 instances/tile on average).
• Studying the value of additional multispectral bands (NIR, red-edge) over RGB-only input for weed/crop segmentation.
• Studying model robustness/generalization across growth stages, since the same spatial locations were repeatedly imaged at different phenological stages.
• Research on uncertainty quantification and model calibration for agricultural segmentation, as demonstrated by the original authors with MC-dropout DeepLabv3+.
• Precision/site-specific weed management (SSWM) research, weed cover estimation for plant-science/herbicide-efficacy studies, and cross-domain-generalization research in combination with other weed-segmentation datasets.

Out-of-Scope Use

• General-purpose object detection/segmentation outside agricultural weed/crop monitoring — the dataset is narrowly scoped to one field, one crop, and five plant classes.
• Deployment as a sole training source for autonomous spraying/weeding systems in fields, crop varieties, climates, or growth stages not represented here (single field in Potsdam, Germany; maize only; V1 through near-full-canopy stages of a single 2023 season) without additional domain adaptation or validation, as noted by the original authors' own out-of-distribution experiments.
• Any use implying the dataset represents a random or exhaustive sample of weed species — weed_other pools together multiple lower-frequency species that were not individually annotated.

Dataset Structure

This is a flat image dataset (media_type="image") of 156 samples, split into train (104), val (26), and test (26) via the official spatial split provided by the authors (splits/{train,val,test}.txt, keyed by tile stem) applied as FiftyOne sample tags. The split is spatial rather than random: images from the same one of 48 sampled field locations never appear in more than one split, even though several locations were imaged on multiple dates.

Each sample corresponds to one annotated 600x600 px tile (one date x one location). Field table:

Field	FiftyOne type	Description
filepath	StringField	8-bit RGB composite PNG (derived), used as the primary/grid media
tags	ListField(StringField)	"train" / "val" / "test", from the official spatial split files
ndvi	Heatmap	Derived NDVI heatmap (map_path to an 8-bit stretched NDVI PNG, range=[0, 255])
date	StringField	Acquisition date, one of 2023-05-25, 2023-05-30, 2023-06-06, 2023-06-15 (verbatim from source directory name)
tile_id	StringField	Tile/location identifier (verbatim from source filename stem)
ground_truth_semantic	Segmentation	Per-pixel 6-class mask (mask_path points to the original, untouched semantics PNG)
ground_truth	Detections	One Detection per plant instance, with bounding box, per-instance mask, class label, and a custom plant_instance_id int attribute

Earlier versions of this dataset also carried nir_filepath/re_filepath (8-bit stretched NIR/red-edge display PNGs, as alternate App media) and raw_r_path/raw_g_path/raw_b_path/raw_nir_path/raw_re_path (pointers to the original 16-bit single-band PNGs) as plain StringField sample fields. These have been removed: the raw_*_path fields are not FiftyOne media/label fields, so on export their values were serialized as literal local absolute paths from the machine that built the dataset — meaningless once shared. They've been dropped in favor of keeping the schema clean and portable; the raw 16-bit bands and NIR/RE imagery are still available from the original GFZ data release for anyone who needs them directly.

metadata, id, created_at, and last_modified_at are standard FiftyOne bookkeeping fields (metadata is left uncomputed/None by default; populate it with dataset.compute_metadata() if needed).

Label types and why:

• ground_truth_semantic is a Segmentation because the source annotations are dense per-pixel class-index masks (uint8, values 0-5) — one label per pixel, directly compatible with FiftyOne's semantic segmentation label type.
• ground_truth is a Detections, with one Detection per plant instance, because the source instance masks are per-instance polygons converted to per-instance boolean masks with tight bounding boxes — the natural FiftyOne representation for instance segmentation. Each Detection.mask is cropped to its bounding box (the FiftyOne convention), and each carries a plant_instance_id attribute preserving the original instance ID from the source instance mask, in case anyone needs to cross-reference back to the raw instance PNG.

Semantic/instance classes (dataset.mask_targets["ground_truth_semantic"] and dataset.default_mask_targets):

ID	Class
0	background
1	maize (crop)
2	amaranth
3	barnyard_grass
4	quickweed
5	weed_other

dataset.info stores dataset-level provenance: source (original data release URL), paper, code (GitHub repo), citation, classes (the ID-to-name mapping above), bands (["R", "G", "B", "NIR", "RE"]), and notes describing the filepath/raw-path convention below.

App configuration: dataset.app_config.media_fields is set to ["filepath"] with grid_media_field="filepath", so the FiftyOne App grid displays the RGB composite.

Parsing decisions:

• The source bands are 16-bit single-channel PNGs, which are not directly viewable as standard 8-bit imagery. filepath (RGB composite) and the ndvi heatmap are derived 8-bit renderings computed from these raw bands purely for display/browsing.
• The RGB 8-bit rendering uses a fixed global linear stretch (clipping to [8000, 50000] of the raw 16-bit range) chosen from percentile sampling across the dataset, so brightness/contrast is comparable across tiles and dates.
• NDVI is computed as (NIR - R) / (NIR + R) from the raw bands (theoretical range [-1, 1]), then stretched over [-0.3, 0.5] for the 8-bit heatmap rendering — the range that was found (via percentile sampling) to actually separate soil/background from vegetation in this dataset; stretching the full theoretical range made soil-vs-plant contrast nearly invisible.
• The source semantic class indices (0-5) were confirmed by cross-referencing instance/semantic mask overlap counts against Table 2 of the paper.
• Per-instance class labels are not stored directly in the source instance masks — they only encode instance IDs (0 = background, 1..N = instances). Each instance's label was assigned by taking the majority-vote semantic class among the pixels of that instance in the corresponding semantic mask.

Dataset Creation

Curation Rationale

At the time of the source paper, publicly available datasets for weed segmentation were limited in size, sensing modality (mostly RGB-only), crop coverage, and weed species diversity, and none provided UAV-based pixel-level crop/weed annotations specifically for maize — one of the highest-production cereal crops worldwide. The authors created WeedsGalore to fill this gap: a UAV, multispectral (RGB + red-edge + NIR), multitemporal dataset for maize fields with dense semantic and instance annotations across multiple weed species, at markedly higher plant-instance density than prior datasets (e.g. ~78 instances/tile vs. ~5-9 for comparable prior work).

Source Data

Data Collection and Processing

The field (Marquardt, Potsdam, Germany, ~1840 m²) was managed with normal farming practices (no experimental interference); maize was planted May 9, 2023 (70 cm row spacing, 20 cm intra-row spacing), irrigated June 6, and herbicide-treated June 12. Weeds occurred naturally, dominated by common amaranth (Amaranthus retroflexus) and barnyard grass (Echinochloa crus-galli), with quickweed (Galinsoga parviflora) least common; other minor species were pooled into weed_other.

Imagery was captured with a DJI Phantom P4 Multispectral UAV (5 monochrome sensors: Blue 450±16 nm, Green 560±16 nm, Red 650±16 nm, Red-Edge 730±16 nm, NIR 840±26 nm, 2.08 MP each) at 5 m flight height (2.5 mm GSD), with 70% side / 60% front overlap in Hover&Capture mode, across 4 campaigns (May 25, May 30, June 6, June 15, 2023). Rather than annotating an orthomosaic (which the authors found introduces stitching artifacts that would corrupt fine-grained masks), the 5 single-band images per shot were aligned and center-cropped to 600x600 px, and these raw per-shot crops were what got annotated. Orthomosaics were still generated per campaign (via Agisoft Metashape) solely to plan/structure the spatial sampling of annotation locations, and are included in the original source release as additional unlabeled data (not part of this FiftyOne parse).

The field was divided into 12 equal-area patches, with 4 locations randomly sampled per patch (48 locations total). 36 locations were annotated for the first 3 dates (pre-herbicide, most relevant for weed ID); the remaining 12 were annotated across all 4 dates (including late-stage, post-herbicide growth, useful for mechanical weeding / weed-cover analysis). Data were spatially split 70:15:15 (8/2/2 patches) into train/val/test so that no location appears in more than one split.

Who are the source data producers?

The UAV data were collected by the paper's authors' team (GFZ German Research Centre for Geosciences and collaborators) at a working agricultural field in Marquardt, Potsdam, Germany, farmed under normal (non-experimental) practices.

Annotations

Annotation process

Images were annotated using the web-based tool Encord with an AI-assisted, SAM (Segment Anything Model) point-prompt workflow: annotators clicked on each plant to get an initial SAM-predicted mask, then manually refined the polygon boundary. Each plant instance's polygon (i.e. instance mask) was also assigned one of the semantic classes: maize, amaranth, barnyard grass, quickweed, or weed_other (the latter used for rarer species, or when species could not be confidently distinguished due to similar early-growth-stage phenotypes). All 156 images were mutually reviewed by three experts to ensure annotation quality.

Who are the annotators?

Two annotators produced the initial instance/semantic annotations, with review by three domain experts, as reported by the dataset authors.

Personal and Sensitive Information

None. The dataset consists exclusively of overhead multispectral imagery of an agricultural field, plants, and soil — it contains no personal, sensitive, or identifiable human information.

Citation

BibTeX:

@inproceedings{celikkan2025weedsgalore,
  title     = {WeedsGalore: A Multispectral and Multitemporal UAV-Based Dataset for
               Crop and Weed Segmentation in Agricultural Maize Fields},
  author    = {Celikkan, Ekin and Kunzmann, Timo and Yeskaliyev, Yertay and
               Itzerott, Sibylle and Klein, Nadja and Herold, Martin},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of
               Computer Vision (WACV)},
  pages     = {4767--4777},
  year      = {2025}
}

APA:

Celikkan, E., Kunzmann, T., Yeskaliyev, Y., Itzerott, S., Klein, N., & Herold, M. (2025). WeedsGalore: A Multispectral and Multitemporal UAV-Based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields. WACV 2025, 4767-4777.

More Information

The source paper additionally provides extensive semantic/instance segmentation baselines (DeepLabv3+, MaskFormer, RGB vs. multispectral input), a probabilistic (MC-dropout) variant for calibrated uncertainty quantification, and an out-of-distribution generalization study on a separate unseen field ("Maize2024") — see arxiv.org/abs/2502.13103 for full results and discussion. Code for the original baselines is available at github.com/GFZ/weedsgalore.

Dataset Card Authors

Harpreet Sahota (FiftyOne parse and dataset card)

Dataset Card Contact

harpreetsahota