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.

Uses

# 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")

# Step 3: Explore the dataset splits
# The dataset contains three splits: "train", "val", and "test"
print("Available splits:", dataset.keys())

# Step 4: Access individual examples
# Each example contains an image and a corresponding annotation (segmentation mask)
train_data = dataset["train"]

# View the number of samples in the training set
print("Number of training samples:", len(train_data))

# Step 5: Access a single data sample
# Each data sample has two keys: "image" and "annotation"
sample = train_data[0]

# Step 6: Display the image and annotation
# Use PIL to open and display the image and annotation
image = sample["image"]
annotation = sample["annotation"]

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

# Display the annotation
print("Displaying the segmentation mask...")
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

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

Citation

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.

Dataset Card Contact

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