Coastline Segmentation

This project uses the Sentinel-2 Water Edges Dataset (SWED) to train a U-Net model for coastline segmentation.

Directory Structure

-data/: Contains training and test data.

-train/: Directory for training images and labels.

-`images/`: Directory for training images in `.npy` format.

-`labels/`: Directory for training labels in `.npy` format.

-test/: Directory for test images and labels.

-`images/`: Directory for test images in `.npy` format.

-`labels/`: Directory for test labels in `.npy` format.

-models/: Contains model definitions and saved models.

-unet.py: Defines the U-Net architecture used for segmentation.

-train.py: Script to train the U-Net model.

-evaluate.py: Script to evaluate the model on the test dataset.

-display_samples.py: Script to visualize sample images, labels, and predictions.

-requirements.txt: Lists the dependencies required for the project.

-.gitignore: Specifies files and directories to be ignored by Git.

Dataset

The Sentinel-2 Water Edges Dataset (SWED) is a dataset designed for deep learning applications in image segmentation. It contains annotated Sentinel-2 imagery with pairs of images and corresponding segmentation labels. The labels contain two classes indicated by binary values – water (1) and non-water (0).

You can download the dataset from the UK Hydrographic Office under the Geospatial Commission Data Exploration license.

Downloading the Dataset

1.Download the full dataset (approx. 18GB) or a smaller sample (approx. 1.36GB) from the provided link.

2. Extract the dataset into the data/ directory, ensuring the following structure:

coastline_segmentation/

│

├── data/

│   ├── train/

│   │   ├── images/

│   │   └── labels/

│   ├── test/

│   │   ├── images/

│   │   └── labels/

Setup

1.Clone the repository:

   git clone https://github.com/yourusername/coastline_segmentation.git

   cd coastline_segmentation

2.Install the dependencies:

   pip install -r requirements.txt

3.Prepare the data:

Ensure that your data/train and data/test directories contain the .npy files for images and labels as described in the dataset section.

Training the Model

To train the U-Net model, run:

pythontrain.py

This will train the model using the data in data/train/images and data/train/labels, and save the trained model to models/coastline_segmentation_model.h5.

Evaluating the Model

To evaluate the model on the test dataset, run:

pythonevaluate.py

This will load the test data from data/test/images and data/test/labels, evaluate the model, and display sample predictions.

Visualizing Sample Data

To visualize sample images, labels, and predictions, run:

pythondisplay_samples.py

File Descriptions

data/process_data.py

Responsible for loading and preprocessing the dataset.

models/unet.py

Defines the U-Net architecture used for segmentation.

train.py

Script to train the U-Net model.

evaluate.py

Script to evaluate the trained model on the test dataset.

display_samples.py

Script to visualize sample images, labels, and predictions.

requirements.txt

Lists the dependencies required for the project.

.gitignore

Specifies files and directories to be ignored by Git, including data/train and data/test.

License

The Sentinel-2 Water Edges Dataset (SWED) is licensed under the terms specified by the UK Hydrographic Office for the Sentinel-2 Water Edges Dataset (SWED). For more details, please refer to the dataset license available at UKHO Open Marine Data Portal.

The code of coastline-ML is i dunno yet.

Feel free to contribute to this project by submitting issues or pull requests. For any questions or inquiries, please contact [your email].