metadata

license: cc-by-nc-4.0
dataset_info:
  features:
    - name: image
      dtype: image
    - name: annotation
      dtype: image
  splits:
    - name: train
      num_bytes: 8683872818.848
      num_examples: 45728
    - name: val
      num_bytes: 1396718238.836
      num_examples: 15358
    - name: test
      num_bytes: 1516829621.65
      num_examples: 4623
  download_size: 12492798567
  dataset_size: 11597420679.334
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: val
        path: data/val-*
      - split: test
        path: data/test-*

Dataset Card for Cloud-Adapter

This dataset card aims to describe the datasets used in the Cloud-Adapter, a collection of high-resolution satellite images and semantic segmentation masks for cloud detection and related tasks.

Install

pip install huggingface-hub

Usage

# Step 1: Download datasets
huggingface-cli download --repo-type dataset XavierJiezou/Cloud-Adapter --local-dir data --include hrc_whu.zip 
huggingface-cli download --repo-type dataset XavierJiezou/Cloud-Adapter --local-dir data --include gf12ms_whu_gf1.zip
huggingface-cli download --repo-type dataset XavierJiezou/Cloud-Adapter --local-dir data --include gf12ms_whu_gf2.zip
huggingface-cli download --repo-type dataset XavierJiezou/Cloud-Adapter --local-dir data --include cloudsen12_high_l1c.zip
huggingface-cli download --repo-type dataset XavierJiezou/Cloud-Adapter --local-dir data --include cloudsen12_high_l2a.zip
huggingface-cli download --repo-type dataset XavierJiezou/Cloud-Adapter --local-dir data --include l8_biome.zip

# Step 2: Extract datasets
unzip hrc_whu.zip -d hrc_whu
unzip gf12ms_whu_gf1.zip -d gf12ms_whu_gf1
unzip gf12ms_whu_gf2.zip -d gf12ms_whu_gf2
unzip cloudsen12_high_l1c.zip -d cloudsen12_high_l1c
unzip cloudsen12_high_l2a.zip -d cloudsen12_high_l2a
unzip l8_biome.zip -d l8_biome

Example

import os
import zipfile
from huggingface_hub import hf_hub_download

# Define the dataset repository
repo_id = "XavierJiezou/Cloud-Adapter"
# Select the zip file of the dataset to download
zip_files = [
    "hrc_whu.zip",
    # "gf12ms_whu_gf1.zip",
    # "gf12ms_whu_gf2.zip",
    # "cloudsen12_high_l1c.zip",
    # "cloudsen12_high_l2a.zip",
    # "l8_biome.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

hrc_whu: https://github.com/dr-lizhiwei/HRC_WHU
gf12ms_whu: https://github.com/whu-ZSC/GF1-GF2MS-WHU
cloudsen12_high: https://huggingface.co/datasets/csaybar/CloudSEN12-high
l8_biome: https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data

Citation

@article{hrc_whu,
title = {Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {150},
pages = {197-212},
year = {2019},
author = {Zhiwei Li and Huanfeng Shen and Qing Cheng and Yuhao Liu and Shucheng You and Zongyi He},
}

@article{gf12ms_whu,
  author={Zhu, Shaocong and Li, Zhiwei and Shen, Huanfeng},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Transferring Deep Models for Cloud Detection in Multisensor Images via Weakly Supervised Learning}, 
  year={2024},
  volume={62},
  pages={1-18},
}

@article{cloudsen12_high,
  title={CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2},
  author={Aybar, Cesar and Ysuhuaylas, Luis and Loja, Jhomira and Gonzales, Karen and Herrera, Fernando and Bautista, Lesly and Yali, Roy and Flores, Angie and Diaz, Lissette and Cuenca, Nicole and others},
  journal={Scientific data},
  volume={9},
  number={1},
  pages={782},
  year={2022},
}

@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.