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--- |
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license: mit |
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task_categories: |
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- object-detection |
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language: |
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- en |
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tags: |
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- remote_sensing |
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- NASA_IMPACT |
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- YOLO |
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- Cloud_image |
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- Turbulance |
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- Transverse_Cirrus_Bands |
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pretty_name: Manually Annotated Data Set for Transverse Cirrus Bands |
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size_categories: |
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- 100M<n<1B |
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--- |
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# Transverse Cirrus Bands (TCB) Dataset |
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## Dataset Overview |
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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. |
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## Data Collection |
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- **Source**: NASA-IMPACT Data Share |
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- **Satellite Sensors**: VIIRS (Visible Infrared Imaging Radiometer Suite), MODIS (Moderate Resolution Imaging Spectroradiometer) |
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- **Acquisition Method**: Downloaded via AWS S3 |
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## Annotation Details |
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- **Format**: YOLO (.txt) and COCO (.json) |
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- **Bounding Box Labels**: Transverse Cirrus Bands (TCB) |
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- **Annotation Tool**: MakeSense.ai |
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- **Total Images**: X (To be specified) |
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- **Train/Validation/Test Split**: 70% / 20% / 10% |
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## File Structure |
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``` |
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TCB_Dataset/ |
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│── images/ |
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│ ├── train/ |
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│ ├── val/ |
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│ ├── test/ |
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│── labels/ |
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│ ├── train/ |
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│ ├── val/ |
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│ ├── test/ |
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│── annotations/ |
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│ ├── COCO_format.json |
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│── README.md |
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``` |
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## Potential Applications |
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- **Turbulence Detection**: Enhancing aviation safety by predicting turbulence-prone regions. |
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- **AI-based Weather Prediction**: Training deep learning models for real-time cloud pattern analysis. |
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- **Climate Research**: Studying the impact of TCBs on atmospheric dynamics and climate change. |
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- **Satellite-based Hazard Assessment**: Detecting and monitoring extreme weather events. |
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## How to Use |
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1. Clone the repository: |
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```bash |
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git clone <repo_link> |
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``` |
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2. Load images and annotations into your object detection model pipeline. |
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3. Train models using **YOLOv8** or any compatible object detection framework. |
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## Citation |
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If you use this dataset in your research, please cite: |
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``` |
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@article{TCB_Dataset2024, |
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title={A Manually Annotated Dataset of Transverse Cirrus Bands for Object Detection in Satellite Imagery}, |
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author={Your Name}, |
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year={2024}, |
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journal={Hugging Face Dataset Repository} |
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} |
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``` |
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## License |
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mit |
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--- |
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This dataset is open for contributions. Feel free to submit pull requests or raise issues for improvements! |
