---
license: mit
task_categories:
- object-detection
language:
- en
tags:
- remote_sensing
- NASA_IMPACT
- YOLO
- Cloud_image
- Turbulance
- Transverse_Cirrus_Bands
pretty_name: Manually Annotated Data Set for Transverse Cirrus Bands
size_categories:
- 100M<n<1B
---
# Transverse Cirrus Bands (TCB) Dataset

## Dataset Overview

This dataset contains manually annotated satellite imagery of **Transverse Cirrus Bands (TCBs)**, a type of cloud formation often associated with atmospheric turbulence. The dataset is formatted for object detection tasks using the **YOLO** and **COCO** annotation formats, making it suitable for training deep learning models for automated TCB detection.

## Data Collection

- **Source**: NASA-IMPACT Data Share
- **Satellite Sensors**: VIIRS (Visible Infrared Imaging Radiometer Suite), MODIS (Moderate Resolution Imaging Spectroradiometer)
- **Acquisition Method**: Downloaded via AWS S3

## Annotation Details

- **Format**: YOLO (.txt) and COCO (.json)
- **Bounding Box Labels**: Transverse Cirrus Bands (TCB)
- **Annotation Tool**: MakeSense.ai
- **Total Images**: X (To be specified)
- **Train/Validation/Test Split**: 70% / 20% / 10%

## File Structure

```
TCB_Dataset/
│── images/
│   ├── train/
│   ├── val/
│   ├── test/
│── labels/
│   ├── train/
│   ├── val/
│   ├── test/
│── annotations/
│   ├── COCO_format.json
│── README.md
```

## Potential Applications

- **Turbulence Detection**: Enhancing aviation safety by predicting turbulence-prone regions.
- **AI-based Weather Prediction**: Training deep learning models for real-time cloud pattern analysis.
- **Climate Research**: Studying the impact of TCBs on atmospheric dynamics and climate change.
- **Satellite-based Hazard Assessment**: Detecting and monitoring extreme weather events.

## How to Use

1. Clone the repository:
   ```bash
   git clone <repo_link>
   ```
2. Load images and annotations into your object detection model pipeline.
3. Train models using **YOLOv8** or any compatible object detection framework.

## Citation

If you use this dataset in your research, please cite:

```
@article{TCB_Dataset2024,
  title={A Manually Annotated Dataset of Transverse Cirrus Bands for Object Detection in Satellite Imagery},
  author={Your Name},
  year={2024},
  journal={Hugging Face Dataset Repository}
}
```

## License

mit

---

This dataset is open for contributions. Feel free to submit pull requests or raise issues for improvements!