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Notebook","category":"Hydrosphere","sub_category":"Ocean Models","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"![magicbathynet](https://github.com/pagraf/MagicBathyNet/assets/35768562/85d2f09c-2bc9-4edb-9b3e-5428589fdf64)\n\n\n# MagicBathyNet: A Multimodal Remote Sensing Dataset for Benchmarking Learning-based Bathymetry and Pixel-based Classification in Shallow Waters\n\n[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.\n\u003cbr /\u003e\n\u003cbr /\u003e\n[![MagicBathy](https://img.shields.io/badge/MagicBathy-Project-red.svg)](https://www.magicbathy.eu) \u003cbr /\u003e\nDOI of GitHub Repository [![DOI](https://zenodo.org/badge/748123214.svg)](https://doi.org/10.5281/zenodo.15630601)\n\u003cbr /\u003e\n\n# Package for benchmarking MagicBathyNet dataset in learning-based bathymetry and pixel-based classification.\n\nThis 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.\"\u003cbr /\u003e\n\n[![arXiv](https://img.shields.io/badge/arXiv-Paper-\u003cCOLOR\u003e.svg)](https://arxiv.org/abs/2405.15477) [![IEEE](https://img.shields.io/badge/IEEE-Paper-blue.svg)](https://ieeexplore.ieee.org/document/10641355)\n\n## Citation\n\nIf you find this repository useful, please consider giving a star ⭐.\u003cbr /\u003e\nIf you use the code in this repository or the dataset please cite:\n\n\u003eP. 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.\n```\n@INPROCEEDINGS{10641355,\n  author={Agrafiotis, Panagiotis and Janowski, Łukasz and Skarlatos, Dimitrios and Demir, Begüm},\n  booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium}, \n  title={MAGICBATHYNET: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-Based Classification in Shallow Waters}, \n  year={2024},\n  volume={},\n  number={},\n  pages={249-253},\n  doi={10.1109/IGARSS53475.2024.10641355}}\n```\n\u003cbr /\u003e\n\n# Getting started\n\n## Downloading the dataset\n\nFor 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)\n\n\n## Dataset structure\nThe folder structure should be as follows:\n```\n┗ 📂 magicbathynet/\n  ┣ 📂 agia_napa/\n  ┃ ┣ 📂 img/\n  ┃ ┃ ┣ 📂 aerial/\n  ┃ ┃ ┃ ┣ 📜 img_339.tif\n  ┃ ┃ ┃ ┣ 📜 ...\n  ┃ ┃ ┣ 📂 s2/\n  ┃ ┃ ┃ ┣ 📜 img_339.tif\n  ┃ ┃ ┃ ┣ 📜 ...\n  ┃ ┃ ┣ 📂 spot6/\n  ┃ ┃ ┃ ┣ 📜 img_339.tif\n  ┃ ┃ ┃ ┣ 📜 ...\n  ┃ ┣ 📂 depth/\n  ┃ ┃ ┣ 📂 aerial/\n  ┃ ┃ ┃ ┣ 📜 depth_339.tif\n  ┃ ┃ ┃ ┣ 📜 ...\n  ┃ ┃ ┣ 📂 s2/\n  ┃ ┃ ┃ ┣ 📜 depth_339.tif\n  ┃ ┃ ┃ ┣ 📜 ...\n  ┃ ┃ ┣ 📂 spot6/\n  ┃ ┃ ┃ ┣ 📜 depth_339.tif\n  ┃ ┃ ┃ ┣ 📜 ...\n  ┃ ┣ 📂 gts/\n  ┃ ┃ ┣ 📂 aerial/\n  ┃ ┃ ┃ ┣ 📜 gts_339.tif\n  ┃ ┃ ┃ ┣ 📜 ...\n  ┃ ┃ ┣ 📂 s2/\n  ┃ ┃ ┃ ┣ 📜 gts_339.tif\n  ┃ ┃ ┃ ┣ 📜 ...\n  ┃ ┃ ┣ 📂 spot6/\n  ┃ ┃ ┃ ┣ 📜 gts_339.tif\n  ┃ ┃ ┃ ┣ 📜 ...\n  ┃ ┣ 📜 [modality]_split_bathymetry.txt\n  ┃ ┣ 📜 [modality]_split_pixel_class.txt\n  ┃ ┣ 📜 norm_param_[modality]_an.txt\n  ┃\n  ┣ 📂 puck_lagoon/\n  ┃ ┣ 📂 img/\n  ┃ ┃ ┣ 📜 ...\n  ┃ ┣ 📂 depth/\n  ┃ ┃ ┣ 📜 ...\n  ┃ ┣ 📂 gts/\n  ┃ ┃ ┣ 📜 ...\n  ┃ ┣ 📜 [modality]_split_bathymetry.txt\n  ┃ ┣ 📜 [modality]_split_pixel_class.txt\n  ┃ ┣ 📜 norm_param_[modality]_pl.txt\n```\nThe mapping between RGB color values and classes is:\n\n```\nFor the Agia Napa area:\n0 : (0, 128, 0),   #seagrass\n1 : (0, 0, 255),   #rock\n2 : (255, 0, 0),   #macroalgae\n3 : (255, 128, 0), #sand\n4 : (0, 0, 0)}     #Undefined (black)\n\nFor the Puck Lagoon area:\n0 : (255, 128, 0), #sand\n1 : (0, 128, 0) ,  #eelgrass/pondweed\n2 : (0, 0, 0)}     #Undefined (black)\n```\n\n## Clone the repo\n\n`git clone https://github.com/pagraf/MagicBathyNet.git`\n\n## Installation Guide\nThe requirements are easily installed via Anaconda (recommended):\n\n`conda env create -f environment.yml`\n\nAfter the installation is completed, activate the environment:\n\n`conda activate magicbathynet`\n\nOpen Jupyter Notebook:\n\n`jupyter notebook`\n\n## Train and Test the models\nTo train and test the **bathymetry** models use **MagicBathyNet_bathymetry.ipynb**.\n\nTo train and test the **pixel-based classification** models use **MagicBathyNet_pixelclass.ipynb**.\n\n## Pre-trained Deep Learning Models\nWe 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:\n\n### Pixel-based classification\n| Model Names | Modality | Area | Pre-Trained PyTorch Models                                                                                                                | \n| ----------- |----------| ---- |----------------------------------------------------------------------------------------------------------------------------------------------|\n| U-Net | Aerial | Agia Napa | [unet_aerial_an.zip](https://drive.google.com/file/d/1vrYwOGEPbiuyvAmtE8-SfbDfzVWU8oMD/view?usp=sharing) |\n| SegFormer | Aerial | Agia Napa         | [segformer_aerial_an.zip](https://drive.google.com/file/d/1rUr_KvAgOKwBmykLoprUy4Aw4fCiYGIm/view?usp=sharing)            |\n| U-Net | Aerial | Puck Lagoon | [unet_aerial_pl.zip](https://drive.google.com/file/d/1PVIRvFpiw4xf6xgLCF4Bzhpb_2wD3Q3G/view?usp=sharing) |\n| SegFormer | Aerial | Puck Lagoon         | [segformer_aerial_pl.zip](https://drive.google.com/file/d/1c_YNKXvANd71piMEmGOa1hdo6J58ZynP/view?usp=sharing)            |\n| U-Net | SPOT-6 | Agia Napa        | [unet_spot6_an.zip](https://drive.google.com/file/d/1GNpk6zG-t0e853B5ntd5ETJEeyShnMkL/view?usp=sharing)            |\n| SegFormer | SPOT-6 | Agia Napa      | [segformer_spot6_an.zip](https://drive.google.com/file/d/198k981Qdvw8Y5eZ8sMGVDIcPC8B-uDkx/view?usp=sharing)      |\n| U-Net | SPOT-6 | Puck Lagoon        | [unet_spot6_pl.zip](https://drive.google.com/file/d/1h3jZ-QY8xiI6Q4O7jXO3BzZDyO08W1A0/view?usp=sharing)            |\n| SegFormer | SPOT-6 | Puck Lagoon      | [segformer_spot6_pl.zip](https://drive.google.com/file/d/12p4AdmHgK0PWsxJ6x2pedqqzfkJ2f4E-/view?usp=sharing )      |\n| U-Net | Sentinel-2 | Agia Napa     | [unet_s2_an.zip](https://drive.google.com/file/d/19FeZ60AK67z-DCFWhjqDrV6-Xlyicgqn/view?usp=sharing)   | \n| SegFormer | Sentinel-2 | Agia Napa    | [segformer_s2_an.zip](https://drive.google.com/file/d/1nOKhVecid3yjAg0fF-rTV2-cGmUMj6nh/view?usp=sharing)   |\n| U-Net | Sentinel-2 | Puck Lagoon    | [unet_s2_pl.zip](https://drive.google.com/file/d/1iySJta5qPegW7TEy5yIiFvqr816THEuY/view?usp=sharing)   | \n| SegFormer | Sentinel-2 | Puck Lagoon    | [segformer_s2_pl.zip](https://drive.google.com/file/d/1T7iP7khBBK0YsQzdUHAUksX9Q_Qbw5kW/view?usp=sharing)   |\n\n### Learning-based Bathymetry\n| Model Name | Modality | Area | Pre-Trained PyTorch Models                                                                                                                | \n| ----------- |----------| ---- |----------------------------------------------------------------------------------------------------------------------------------------------|\n| Modified U-Net for bathymetry | Aerial | Agia Napa | [bathymetry_aerial_an.zip](https://drive.google.com/file/d/1-qUlQMHdZDZKkeQ4RLX54o4TK6juwOqD/view?usp=sharing) |\n| Modified U-Net for bathymetry | Aerial | Puck Lagoon         | [bathymetry_aerial_pl.zip](https://drive.google.com/file/d/1SN8YH-WZIdR4e5Zl0uQK4OM62z_WNCks/view?usp=sharing)            |\n| 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)            |\n| 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)      |\n| Modified U-Net for bathymetry | Sentinel-2 | Agia Napa    | [bathymetry_s2_an.zip](https://drive.google.com/file/d/15esoghCHHHilQJxTBBjmHpAAde-AHdtE/view?usp=sharing)   | \n| Modified U-Net for bathymetry | Sentinel-2 | Puck Lagoon    | [bathymetry_s2_pl.zip](https://drive.google.com/file/d/1oCnD5ePwVW3ORix4GWRcMUp_kSL5p9Se/view?usp=sharing)   |\n\nTo 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.\n\n## Example testing results\nExample 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/). \n\n\n![img_410_aerial](https://github.com/pagraf/MagicBathyNet/assets/35768562/132b4166-b012-476b-9653-b511ede2c6f3)\n![aerial_410_unet](https://github.com/pagraf/MagicBathyNet/assets/35768562/8a293815-87b4-4f45-b5de-c99f7c827bb5)\n![depth_410_aerial](https://github.com/pagraf/MagicBathyNet/assets/35768562/7995efd7-f85e-4411-8037-4a68c9780bfb)\n\n\n\n## Authors\nPanagiotis Agrafiotis [http://users.ntua.gr/pagraf/](http://users.ntua.gr/pagraf/)\n\n## Feedback\nFeel free to give feedback, by sending an email to: pagraf@central.ntua.gr\n\u003cbr /\u003e\n\u003cbr /\u003e\n\n# Funding\nThis 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/).\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.5281/zenodo.15630601"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/305633","html_url":"https://ost.ecosyste.ms/projects/305633"}