Datasets:

little-king
/

Plot2Phenome

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Plot2Phenome Preview Dataset

This is a preview subset of the Plot2Phenome dataset, intended for pre-publication release. The full dataset will be made available upon paper acceptance.

Overview

Plot2Phenome is a multi-modal UAV remote sensing dataset for breeding plot segmentation. Each sample consists of an RGB image with polygon annotations delineating individual field plots. This preview contains 150 patches randomly sampled (5 per scene × date) from the full dataset.

Scenes

Scene	Description
01_2020_rugao_wheat	Rugao, Jiangsu — Winter wheat (2020)
02_2020_suining_wheat	Suining, Jiangsu — Winter wheat (2020)
03_2024_rugao_rice	Rugao, Jiangsu — Rice (2024)
04_2025_yandu_wheat	Yandu, Jiangsu — Winter wheat (2025)
05_2025_dengzhou_maize	Dengzhou, Henan — Summer maize (2025)
06_2025_rugao_rice	Rugao, Jiangsu — Rice (2025)

Dataset Structure

plot2phenome_preview/
  data.yaml          # YOLO-seg dataset config
  images/
    train/           # RGB patches (512×512 PNG)
    val/             # RGB patches (512×512 PNG)
  labels/
    train/           # YOLO-seg polygon labels (normalized [0,1])
    val/             # YOLO-seg polygon labels (normalized [0,1])

Label Format

Ultralytics YOLO-seg format. Each .txt file corresponds to one image. Each line encodes one instance polygon:

class_id x1 y1 x2 y2 ... xn yn

class_id is always 0 (plot).
Coordinates are normalized to [0, 1] relative to image width and height.
The polygon is closed (first and last points may be identical).

Splits

Patches are split by flight date (train/val) to avoid spatial leakage. Each scene contains patches from multiple growth stages captured on different dates.

Usage with Ultralytics

from ultralytics import YOLO

model = YOLO('yolov8x-seg.pt')
model.train(data='data.yaml', epochs=100, imgsz=512)

License

This preview dataset is released under CC BY-NC 4.0.

Citation

If you use this dataset, please cite our paper (forthcoming).

Full Dataset

The complete Plot2Phenome dataset includes multi-modal data (RGB + DSM height), additional scenes, and higher sample density. It will be released at here upon paper acceptance.

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