clouds2mask 0.1.5

Python library for cloud and cloud shadow segmentation in Sentinel-2

Welcome to CloudS2Mask ☁️

💡 About

CloudS2Mask is an open-source Python library that efficiently segments clouds and cloud shadows in Sentinel-2 imagery using state-of-the-art deep learning techniques. Benchmarks with the CloudSEN12 Dataset show a 17% error reduction compared to the most competent existing methods.

🎯 Features

• High-precision cloud and cloud shadow segmentation for Sentinel-2 L1C imagery.
• Rapid processing: Approximately 2.2 seconds per scene at 20m resolution (RTX 4090, AMD Ryzen 9 5950X).
• Compatibility with both GPU and non-GPU systems.
• Supported on Linux, Windows, and macOS.

🚀 Installation

Windows Users with NVIDIA GPUs: Before installing CloudS2Mask, ensure you've installed PyTorch with CUDA support, then follow the steps below.

Mac and Linux Users: You can proceed with the installation commands below. To install CloudS2Mask, clone this repository and proceed with manual installation.

git clone https://github.com/DPIRD-DMA/CloudS2Mask
cd CloudS2Mask
pip install -q .
cd ..

💻 Usage

Here's a simple demonstration of how to use CloudS2Mask:

All you need to do is pass a list of Sentinel-2 level 1C 'SAFE' directories to CloudS2Mask.

from pathlib import Path
from clouds2mask import (
    create_settings,
    batch_process_scenes,
)

output_dir = Path("./outputs")
l1c_folders_path = Path("/path/to/your/S2_l1c_SAFE/folders")
l1c_folders = list(l1c_folders_path.glob("*.SAFE"))


scene_settings = create_settings(
    sent_safe_dirs=l1c_folders,
    output_dir=output_dir,
    processing_res=20,
)

paths_to_masks = batch_process_scenes(scene_settings)

⚙️ Performance Tuning

CloudsS2Mask offers a range of performance and accuracy options, here are some examples,

Settings for high accuracy inference:

scene_settings = create_settings(
    sent_safe_dirs=l1c_folders,
    output_dir=output_dir,
    batch_size=32,
    tta_max_depth=5,
    processing_res=10,
    patch_overlap_px=64,
    model_ensembling=True,
)

Settings for fast inference:

scene_settings = create_settings(
    sent_safe_dirs=l1c_folders,
    output_dir=output_dir,
    batch_size=32,
    processing_res=20,
    output_compression=None,
)

Settings for CPU inference:

scene_settings = create_settings(
    sent_safe_dirs=l1c_folders,
    output_dir=output_dir,
    batch_size=1,
    processing_res=20,
)

CloudS2Mask will try to auto detect acceleration cards such as NVIDIA GPUs or Apple MPS, but you can also manually specify them like this:

scene_settings = create_settings(
    sent_safe_dirs=l1c_folders,
    output_dir=output_dir,
    pytorch_device='MPS',
)

👏 Contributing

We welcome all contributions! Feel free to open an issue or submit a pull request.

📄 License

This project is licensed under the MIT License - please refer to the LICENSE file for more details.

📝 Contact

For support, bug reporting, or to contribute, feel free to reach out at [e-mail].