MagicBathyNet: A Multimodal Remote Sensing Dataset for Benchmarking Learning-based Bathymetry and Pixel-based Classification in Shallow Waters
MagicBathyNet 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.
DOI of GitHub Repository 
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."
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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}}
For downloading the dataset and a detailed explanation of it, please visit the MagicBathy Project website at https://www.magicbathy.eu/magicbathynet.html
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)
git clone https://github.com/pagraf/MagicBathyNet.git
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
To train and test the bathymetry models use MagicBathyNet_bathymetry.ipynb.
To train and test the pixel-based classification models use MagicBathyNet_pixelclass.ipynb.
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:
| Model Names | Modality | Area | Pre-Trained PyTorch Models |
|---|---|---|---|
| U-Net | Aerial | Agia Napa | unet_aerial_an.zip |
| SegFormer | Aerial | Agia Napa | segformer_aerial_an.zip |
| U-Net | Aerial | Puck Lagoon | unet_aerial_pl.zip |
| SegFormer | Aerial | Puck Lagoon | segformer_aerial_pl.zip |
| U-Net | SPOT-6 | Agia Napa | unet_spot6_an.zip |
| SegFormer | SPOT-6 | Agia Napa | segformer_spot6_an.zip |
| U-Net | SPOT-6 | Puck Lagoon | unet_spot6_pl.zip |
| SegFormer | SPOT-6 | Puck Lagoon | segformer_spot6_pl.zip |
| U-Net | Sentinel-2 | Agia Napa | unet_s2_an.zip |
| SegFormer | Sentinel-2 | Agia Napa | segformer_s2_an.zip |
| U-Net | Sentinel-2 | Puck Lagoon | unet_s2_pl.zip |
| SegFormer | Sentinel-2 | Puck Lagoon | segformer_s2_pl.zip |
| Model Name | Modality | Area | Pre-Trained PyTorch Models |
|---|---|---|---|
| Modified U-Net for bathymetry | Aerial | Agia Napa | bathymetry_aerial_an.zip |
| Modified U-Net for bathymetry | Aerial | Puck Lagoon | bathymetry_aerial_pl.zip |
| Modified U-Net for bathymetry | SPOT-6 | Agia Napa | bathymetry_spot6_an.zip |
| Modified U-Net for bathymetry | SPOT-6 | Puck Lagoon | bathymetry_spot6_pl.zip |
| Modified U-Net for bathymetry | Sentinel-2 | Agia Napa | bathymetry_s2_an.zip |
| Modified U-Net for bathymetry | Sentinel-2 | Puck Lagoon | bathymetry_s2_pl.zip |
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 while train-evaluation splits are included in the dataset.
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.
Panagiotis Agrafiotis https://www.user.tu-berlin.de/pagraf/
Feel free to give feedback, by sending an email to: agrafiotis@tu-berlin.de
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. For more information about the project visit https://www.magicbathy.eu/.