---
pretty_name: SyntheticGenV5
license: mit
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
tags:
- remote-sensing
- semantic-segmentation
- synthetic-data
- domain-adaptation
- image
size_categories:
- 1K<n<10K
configs:
- config_name: default
  default: true
  drop_labels: true
  data_files:
  - split: train
    path: Train/metadata.csv
---

# 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

```text
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

```python
from datasets import load_dataset

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

## Source

This dataset is derived based on **LoveDA** 

### Your downstream segmentation would work way better if you couple this dataset with original LoveDA dataset ;)

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

## Citation

### SyntheticGenV5 / Associated Paper

```bibtex
@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}
}
```

### LoveDA

```bibtex
@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}, 
}
```



## 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.