Update README.md

README.md

@@ -33,118 +33,145 @@ configs:
 
 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.
 
-## Uses
+## Install
 
-```python
-# Step 1: Install the datasets library
-# Ensure you have the `datasets` library installed
-# You can install it using pip if it's not already installed:
-# pip install datasets
-
-from datasets import load_dataset
-from PIL import Image
-
-# Step 2: Load the Cloud-Adapter dataset
-# Replace "XavierJiezou/Cloud-Adapter" with the dataset repository name on Hugging Face
-dataset = load_dataset("XavierJiezou/Cloud-Adapter")
+```bash
+pip install huggingface-hub
+```
 
-# Step 3: Explore the dataset splits
-# The dataset contains three splits: "train", "val", and "test"
-print("Available splits:", dataset.keys())
+## Usage
+
+```bash
+# 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
+```
 
-# Step 4: Access individual examples
-# Each example contains an image and a corresponding annotation (segmentation mask)
-train_data = dataset["train"]
+## Example
 
-# View the number of samples in the training set
-print("Number of training samples:", len(train_data))
+```python
+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
 
-# Step 5: Access a single data sample
-# Each data sample has two keys: "image" and "annotation"
-sample = train_data[0]
+# 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])
 
-# Step 6: Display the image and annotation
-# Use PIL to open and display the image and annotation
-image = sample["image"]
-annotation = sample["annotation"]
+# Open and display the image
+image = Image.open(image_path)
+annotation = Image.open(annotation_path)
 
-# Display the image
 print("Displaying the image...")
 image.show()
 
-# Display the annotation
-print("Displaying the segmentation mask...")
+print("Displaying the annotation...")
 annotation.show()
-
-# Step 7: Use in a machine learning pipeline
-# You can integrate this dataset into your ML pipeline by iterating over the splits
-for sample in train_data:
-    image = sample["image"]
-    annotation = sample["annotation"]
-    # Process or feed `image` and `annotation` into your ML model here
-
-# Additional Info: Dataset splits
-# - dataset["train"]: Training split
-# - dataset["val"]: Validation split
-# - dataset["test"]: Testing split
 ```
 
-## Dataset Structure
-
-The dataset contains the following splits:
-- `train`: Training images and corresponding segmentation masks.
-- `val`: Validation images and corresponding segmentation masks.
-- `test`: Testing images and corresponding segmentation masks.
-
-Each data point includes:
-- `image`: The input satellite image (PNG or JPG format).
-- `annotation`: The segmentation mask (PNG format).
-
-## Dataset Creation
-
-### Curation Rationale
-
-This dataset was created to facilitate the reproduction of Cloud-Adapter.
-
-### Source Data
+## Source Data
 
-#### Data Collection and Processing
-
-The dataset combines multiple sub-datasets, each processed to ensure consistency in format and organization:
-- Images and annotations were organized into `train`, `val`, and `test` splits.
-- Annotations were verified for accuracy and class consistency.
-
-#### Who are the source data producers?
-
-The dataset combines data from various remote sensing sources. Specific producers are as follows:
-- WHU (gf12ms, hrc)
-- Cloudsen12 dataset
-- L8 Biome dataset
+- 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
 
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-Xavier Jiezou. (2024). *Cloud-Adapter: A Semantic Segmentation Dataset for Remote Sensing Cloud Detection*. Retrieved from https://huggingface.co/datasets/XavierJiezou/Cloud-Adapter.
-
-## Glossary [optional]
-
-[More Information Needed]
-
-## More Information
-
-[More Information Needed]
-
-## Dataset Card Authors
-
-This dataset card was authored by Xavier Jiezou.
+```
+@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}
+}
+```
 
-## Dataset Card Contact
+## Contact
 
 For questions, please contact Xavier Jiezou at xuechaozou (at) foxmail (dot) com.