Datasets:
Tasks:
Image Segmentation
Modalities:
Image
Languages:
English
Tags:
Cloud Detection
Cloud Segmentation
Remote Sensing Images
Satellite Images
HRC-WHU
CloudSEN12-High
License:
File size: 4,190 Bytes
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---
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
```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")
# 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
<!-- 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.
## Dataset Card Contact
For questions, please contact Xavier Jiezou at xuechaozou (at) foxmail (dot) com. |