SyntheticGenV5

SyntheticGenV5 is a synthetic remote-sensing semantic segmentation dataset (from the paper https://huggingface.co/papers/2602.04749) built for Urban–Rural domain-aware learning.

It keeps the original folder layout and uses Train/metadata.csv to connect each image with its semantic mask and RGB mask.

Why use this dataset?

• 🌆 Two domains: Urban and Rural
• 🛰️ Designed for remote-sensing semantic segmentation
• 🧪 Useful for synthetic augmentation and domain generalization
• 👀 Includes RGB mask visualizations for easy inspection

Structure

Train/
├── metadata.csv
├── Urban/
│   ├── image_png/
│   ├── mask_png/
│   └── mask_rgb_png/
└── Rural/
    ├── image_png/
    ├── mask_png/
    └── mask_rgb_png/

Metadata Fields

Each row in Train/metadata.csv contains:

• image_file_name
• mask_file_name
• mask_rgb_file_name
• domain
• source_dataset

Load the dataset

from datasets import load_dataset

ds = load_dataset("buddhi19/SyntheticGenV5")
print(ds["train"][0])

Source

This dataset is derived from or generated based on LoveDA:

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

Citation

LoveDA

@misc{wang2022lovedaremotesensinglandcover,
      title={LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation}, 
      author={Junjue Wang and Zhuo Zheng and Ailong Ma and Xiaoyan Lu and Yanfei Zhong},
      year={2022},
      eprint={2110.08733},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2110.08733}, 
}

SyntheticGenV5 / Associated Paper

@misc{wijenayake2026mitigating,
  title={Mitigating Long-Tail Bias via Prompt-Controlled Diffusion Augmentation},
  author={Buddhi Wijenayake and Nichula Wasalathilake and Roshan Godaliyadda and Vijitha Herath and Parakrama Ekanayake and Vishal M. Patel},
  year={2026},
  eprint={2602.04749},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2602.04749}
}

Notes

• The original directory layout is preserved.
• Train/metadata.csv is used for cleaner loading on Hugging Face.
• RGB masks are included mainly for visualization.
• This release currently contains the train split.

Acknowledgement

We thank the LoveDA authors for the original benchmark that inspired and supported this dataset.