Update README.md

README.md

@@ -1,75 +1,90 @@
-# 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!
-
-
+---
+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!