---
license: cc-by-nc-4.0
task_categories:
- image-segmentation
language:
- en
tags:
- Remote Sensing Images
- Satellite Images
- Grass
- Cloud
---

# Cloud-Adapter-Datasets

This dataset card aims to describe the datasets used in the [KTDA](https://xavierjiezou.github.io/KTDA/), Knowledge Transfer and Domain Adaptation for
Fine-Grained Remote Sensing Image Segmentation

## Install

```bash
pip install huggingface-hub
```

## Usage

```bash
# Step 1: Download datasets
huggingface-cli download --repo-type dataset XavierJiezou/ktda-datasets --local-dir data --include grass.zip 
huggingface-cli download --repo-type dataset XavierJiezou/ktda-datasets --local-dir data --include cloud.zip

# Step 2: Extract datasets
unzip grass.zip -d grass
unzip cloud.zip -d l8_biome
```

## Example

```python
import os
import zipfile
from huggingface_hub import hf_hub_download

# Define the dataset repository
repo_id = "XavierJiezou/ktda-datasets"
# Select the zip file of the dataset to download
zip_files = [
    "grass.zip",
    # "cloud.zip",
]

# Define a directory to extract the datasets
output_dir = "cloud_adapter_paper_data"

# Ensure the output directory exists
os.makedirs(output_dir, exist_ok=True)

# Step 1: Download and extract each ZIP file
for zip_file in zip_files:
    print(f"Downloading {zip_file}...")
    # Download the ZIP file from Hugging Face Hub
    zip_path = hf_hub_download(repo_id=repo_id, filename=zip_file, repo_type="dataset")
    
    # Extract the ZIP file
    extract_path = os.path.join(output_dir, zip_file.replace(".zip", ""))
    with zipfile.ZipFile(zip_path, "r") as zip_ref:
        print(f"Extracting {zip_file} to {extract_path}...")
        zip_ref.extractall(extract_path)

# Step 2: Explore the extracted datasets
# Example: Load and display the contents of the "hrc_whu" dataset
dataset_path = os.path.join(output_dir, "hrc_whu")
train_images_path = os.path.join(dataset_path, "img_dir", "train")
train_annotations_path = os.path.join(dataset_path, "ann_dir", "train")

# Display some files in the training set
print("Training Images:", os.listdir(train_images_path)[:5])
print("Training Annotations:", os.listdir(train_annotations_path)[:5])

# Example: Load and display an image and its annotation
from PIL import Image

# Load an example image and annotation
image_path = os.path.join(train_images_path, os.listdir(train_images_path)[0])
annotation_path = os.path.join(train_annotations_path, os.listdir(train_annotations_path)[0])

# Open and display the image
image = Image.open(image_path)
annotation = Image.open(annotation_path)

print("Displaying the image...")
image.show()

print("Displaying the annotation...")
annotation.show()
```

## Source Data

- l8_biome: https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data

## Citation

```bib

@article{l8_biome,
    title = {Cloud detection algorithm comparison and validation for operational Landsat data products},
    journal = {Remote Sensing of Environment},
    volume = {194},
    pages = {379-390},
    year = {2017},
    author = {Steve Foga and Pat L. Scaramuzza and Song Guo and Zhe Zhu and Ronald D. Dilley and Tim Beckmann and Gail L. Schmidt and John L. Dwyer and M. {Joseph Hughes} and Brady Laue}
}
```

## Contact

For questions, please contact Xavier Jiezou at xuechaozou (at) foxmail (dot) com.