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https://github.com/pagraf/magicbathynet

Quick start guide for benchmarking MagicBathyNet dataset in learning-based bathymetry and pixel-based classification using Remote Sensing imagery.
https://github.com/pagraf/magicbathynet

aerial-imagery bathymetry computer-vision dataset deep-learning depth-estimation earth-observation eu-project magicbathy models ocean-data ocean-mapping remote-sensing satellite-imagery seabed-mapping semantic-segmentation sentinel-2 shallow-water spot6

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Quick start guide for benchmarking MagicBathyNet dataset in learning-based bathymetry and pixel-based classification using Remote Sensing imagery.

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README 

          
![magicbathynet](https://github.com/pagraf/MagicBathyNet/assets/35768562/85d2f09c-2bc9-4edb-9b3e-5428589fdf64)



# MagicBathyNet: A Multimodal Remote Sensing Dataset for Benchmarking Learning-based Bathymetry and Pixel-based Classification in Shallow Waters



[MagicBathyNet](https://www.magicbathy.eu/magicbathynet.html) is a benchmark dataset made up of image patches of Sentinel-2, SPOT-6 and aerial imagery, bathymetry in raster format and seabed classes annotations. Dataset also facilitates unsupervised learning for model pre-training in shallow coastal areas. It is developed in the context of MagicBathy project.







[![MagicBathy](https://img.shields.io/badge/MagicBathy-Project-red.svg)](https://www.magicbathy.eu) 


DOI of GitHub Repository [![DOI](https://zenodo.org/badge/748123214.svg)](https://doi.org/10.5281/zenodo.15630601)





# Package for benchmarking MagicBathyNet dataset in learning-based bathymetry and pixel-based classification.



This repository contains the code of the paper "P. Agrafiotis, Ł. Janowski, D. Skarlatos and B. Demir, "MAGICBATHYNET: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-Based Classification in Shallow Waters," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 249-253, doi: 10.1109/IGARSS53475.2024.10641355."



[![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2405.15477) [![IEEE](https://img.shields.io/badge/IEEE-Paper-blue.svg)](https://ieeexplore.ieee.org/document/10641355)



## Citation



If you find this repository useful, please consider giving a star ⭐.


If you use the code in this repository or the dataset please cite:



>P. Agrafiotis, Ł. Janowski, D. Skarlatos and B. Demir, "MAGICBATHYNET: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-Based Classification in Shallow Waters," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 249-253, doi: 10.1109/IGARSS53475.2024.10641355.

```

@INPROCEEDINGS{10641355,

  author={Agrafiotis, Panagiotis and Janowski, Łukasz and Skarlatos, Dimitrios and Demir, Begüm},

  booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium}, 

  title={MAGICBATHYNET: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-Based Classification in Shallow Waters}, 

  year={2024},

  volume={},

  number={},

  pages={249-253},

  doi={10.1109/IGARSS53475.2024.10641355}}

```





# Getting started



## Downloading the dataset



For downloading the dataset and a detailed explanation of it, please visit the MagicBathy Project website at [https://www.magicbathy.eu/magicbathynet.html](https://www.magicbathy.eu/magicbathynet.html)



## Dataset structure

The folder structure should be as follows:

```

┗ 📂 magicbathynet/

  ┣ 📂 agia_napa/

  ┃ ┣ 📂 img/

  ┃ ┃ ┣ 📂 aerial/

  ┃ ┃ ┃ ┣ 📜 img_339.tif

  ┃ ┃ ┃ ┣ 📜 ...

  ┃ ┃ ┣ 📂 s2/

  ┃ ┃ ┃ ┣ 📜 img_339.tif

  ┃ ┃ ┃ ┣ 📜 ...

  ┃ ┃ ┣ 📂 spot6/

  ┃ ┃ ┃ ┣ 📜 img_339.tif

  ┃ ┃ ┃ ┣ 📜 ...

  ┃ ┣ 📂 depth/

  ┃ ┃ ┣ 📂 aerial/

  ┃ ┃ ┃ ┣ 📜 depth_339.tif

  ┃ ┃ ┃ ┣ 📜 ...

  ┃ ┃ ┣ 📂 s2/

  ┃ ┃ ┃ ┣ 📜 depth_339.tif

  ┃ ┃ ┃ ┣ 📜 ...

  ┃ ┃ ┣ 📂 spot6/

  ┃ ┃ ┃ ┣ 📜 depth_339.tif

  ┃ ┃ ┃ ┣ 📜 ...

  ┃ ┣ 📂 gts/

  ┃ ┃ ┣ 📂 aerial/

  ┃ ┃ ┃ ┣ 📜 gts_339.tif

  ┃ ┃ ┃ ┣ 📜 ...

  ┃ ┃ ┣ 📂 s2/

  ┃ ┃ ┃ ┣ 📜 gts_339.tif

  ┃ ┃ ┃ ┣ 📜 ...

  ┃ ┃ ┣ 📂 spot6/

  ┃ ┃ ┃ ┣ 📜 gts_339.tif

  ┃ ┃ ┃ ┣ 📜 ...

  ┃ ┣ 📜 [modality]_split_bathymetry.txt

  ┃ ┣ 📜 [modality]_split_pixel_class.txt

  ┃ ┣ 📜 norm_param_[modality]_an.txt

  ┃

  ┣ 📂 puck_lagoon/

  ┃ ┣ 📂 img/

  ┃ ┃ ┣ 📜 ...

  ┃ ┣ 📂 depth/

  ┃ ┃ ┣ 📜 ...

  ┃ ┣ 📂 gts/

  ┃ ┃ ┣ 📜 ...

  ┃ ┣ 📜 [modality]_split_bathymetry.txt

  ┃ ┣ 📜 [modality]_split_pixel_class.txt

  ┃ ┣ 📜 norm_param_[modality]_pl.txt

```

The mapping between RGB color values and classes is:



```

For the Agia Napa area:

0 : (0, 128, 0),   #seagrass

1 : (0, 0, 255),   #rock

2 : (255, 0, 0),   #macroalgae

3 : (255, 128, 0), #sand

4 : (0, 0, 0)}     #Undefined (black)



For the Puck Lagoon area:

0 : (255, 128, 0), #sand

1 : (0, 128, 0) ,  #eelgrass/pondweed

2 : (0, 0, 0)}     #Undefined (black)

```



## Clone the repo



`git clone https://github.com/pagraf/MagicBathyNet.git`



## Installation Guide

The requirements are easily installed via Anaconda (recommended):



`conda env create -f environment.yml`



After the installation is completed, activate the environment:



`conda activate magicbathynet`



Open Jupyter Notebook:



`jupyter notebook`



## Train and Test the models

To train and test the **bathymetry** models use **MagicBathyNet_bathymetry.ipynb**.



To train and test the **pixel-based classification** models use **MagicBathyNet_pixelclass.ipynb**.



## Pre-trained Deep Learning Models

We provide code and model weights for the following deep learning models that have been pre-trained on MagicBathyNet for pixel-based classification and bathymetry tasks:



### Pixel-based classification

| Model Names | Modality | Area | Pre-Trained PyTorch Models                                                                                                                | 

| ----------- |----------| ---- |----------------------------------------------------------------------------------------------------------------------------------------------|

| U-Net | Aerial | Agia Napa | [unet_aerial_an.zip](https://drive.google.com/file/d/1vrYwOGEPbiuyvAmtE8-SfbDfzVWU8oMD/view?usp=sharing) |

| SegFormer | Aerial | Agia Napa         | [segformer_aerial_an.zip](https://drive.google.com/file/d/1rUr_KvAgOKwBmykLoprUy4Aw4fCiYGIm/view?usp=sharing)            |

| U-Net | Aerial | Puck Lagoon | [unet_aerial_pl.zip](https://drive.google.com/file/d/1PVIRvFpiw4xf6xgLCF4Bzhpb_2wD3Q3G/view?usp=sharing) |

| SegFormer | Aerial | Puck Lagoon         | [segformer_aerial_pl.zip](https://drive.google.com/file/d/1c_YNKXvANd71piMEmGOa1hdo6J58ZynP/view?usp=sharing)            |

| U-Net | SPOT-6 | Agia Napa        | [unet_spot6_an.zip](https://drive.google.com/file/d/1GNpk6zG-t0e853B5ntd5ETJEeyShnMkL/view?usp=sharing)            |

| SegFormer | SPOT-6 | Agia Napa      | [segformer_spot6_an.zip](https://drive.google.com/file/d/198k981Qdvw8Y5eZ8sMGVDIcPC8B-uDkx/view?usp=sharing)      |

| U-Net | SPOT-6 | Puck Lagoon        | [unet_spot6_pl.zip](https://drive.google.com/file/d/1h3jZ-QY8xiI6Q4O7jXO3BzZDyO08W1A0/view?usp=sharing)            |

| SegFormer | SPOT-6 | Puck Lagoon      | [segformer_spot6_pl.zip](https://drive.google.com/file/d/12p4AdmHgK0PWsxJ6x2pedqqzfkJ2f4E-/view?usp=sharing )      |

| U-Net | Sentinel-2 | Agia Napa     | [unet_s2_an.zip](https://drive.google.com/file/d/19FeZ60AK67z-DCFWhjqDrV6-Xlyicgqn/view?usp=sharing)   | 

| SegFormer | Sentinel-2 | Agia Napa    | [segformer_s2_an.zip](https://drive.google.com/file/d/1nOKhVecid3yjAg0fF-rTV2-cGmUMj6nh/view?usp=sharing)   |

| U-Net | Sentinel-2 | Puck Lagoon    | [unet_s2_pl.zip](https://drive.google.com/file/d/1iySJta5qPegW7TEy5yIiFvqr816THEuY/view?usp=sharing)   | 

| SegFormer | Sentinel-2 | Puck Lagoon    | [segformer_s2_pl.zip](https://drive.google.com/file/d/1T7iP7khBBK0YsQzdUHAUksX9Q_Qbw5kW/view?usp=sharing)   |



### Learning-based Bathymetry

| Model Name | Modality | Area | Pre-Trained PyTorch Models                                                                                                                | 

| ----------- |----------| ---- |----------------------------------------------------------------------------------------------------------------------------------------------|

| Modified U-Net for bathymetry | Aerial | Agia Napa | [bathymetry_aerial_an.zip](https://drive.google.com/file/d/1-qUlQMHdZDZKkeQ4RLX54o4TK6juwOqD/view?usp=sharing) |

| Modified U-Net for bathymetry | Aerial | Puck Lagoon         | [bathymetry_aerial_pl.zip](https://drive.google.com/file/d/1SN8YH-WZIdR4e5Zl0uQK4OM62z_WNCks/view?usp=sharing)            |

| Modified U-Net for bathymetry | SPOT-6 | Agia Napa        | [bathymetry_spot6_an.zip](https://drive.google.com/file/d/1giG-MrJQZ2YLDzjOd2h-u2vr9gfI1jO0/view?usp=sharing)            |

| Modified U-Net for bathymetry | SPOT-6 | Puck Lagoon      | [bathymetry_spot6_pl.zip](https://drive.google.com/file/d/1Cf1gAsEUfACkBep4i_0gB-pp_L0bvaU_/view?usp=sharing)      |

| Modified U-Net for bathymetry | Sentinel-2 | Agia Napa    | [bathymetry_s2_an.zip](https://drive.google.com/file/d/15esoghCHHHilQJxTBBjmHpAAde-AHdtE/view?usp=sharing)   | 

| Modified U-Net for bathymetry | Sentinel-2 | Puck Lagoon    | [bathymetry_s2_pl.zip](https://drive.google.com/file/d/1oCnD5ePwVW3ORix4GWRcMUp_kSL5p9Se/view?usp=sharing)   |



To achieve the results presented in the paper, use the parameters and the specific train-evaluation splits provided in the dataset. Parameters can be found [here](https://drive.google.com/file/d/1gkIG99WFI6LNP7gsRvae9FZWU3blDPgv/view?usp=sharing) while train-evaluation splits are included in the dataset.



## Example testing results

Example patch of the Agia Napa area (left), pixel classification results obtained by U-Net (middle) and predicted bathymetry obtained by MagicBathy-U-Net (right). For more information on the results and accuracy achieved read our [paper](https://www.magicbathy.eu/). 



![img_410_aerial](https://github.com/pagraf/MagicBathyNet/assets/35768562/132b4166-b012-476b-9653-b511ede2c6f3)

![aerial_410_unet](https://github.com/pagraf/MagicBathyNet/assets/35768562/8a293815-87b4-4f45-b5de-c99f7c827bb5)

![depth_410_aerial](https://github.com/pagraf/MagicBathyNet/assets/35768562/7995efd7-f85e-4411-8037-4a68c9780bfb)



## Authors

Panagiotis Agrafiotis [https://www.user.tu-berlin.de/pagraf/](https://www.user.tu-berlin.de/pagraf/)



## Feedback

Feel free to give feedback, by sending an email to: agrafiotis@tu-berlin.de








# Funding

This work is part of **MagicBathy project funded by the European Union’s HORIZON Europe research and innovation programme under the Marie Skłodowska-Curie GA 101063294**. Work has been carried out at the [Remote Sensing Image Analysis group](https://rsim.berlin/). For more information about the project visit [https://www.magicbathy.eu/](https://www.magicbathy.eu/).