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Notebook","category":"Climate Change","sub_category":"Natural Hazard and Storms","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# \u003cimg src=\"https://github.com/kleok/FLOODPY/blob/main/figures/Floodpy_logo.png\" width=\"58\"\u003e FLOODPY - FLOOD PYthon toolbox \n[![GitHub license](https://img.shields.io/badge/License-GNU3-green.svg)](https://github.com/kleok/FLOODPY)\n[![Release](https://img.shields.io/badge/Release-Floodpy_Oct_2024-brightgreen)](https://github.com/kleok/FLOODPY)\n[![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/kleok/FLOODPY/issues)\n[![Documentation](https://readthedocs.org/projects/floodpy/badge/?version=latest)](https://floodpy.readthedocs.io/en/latest/)\n\n## Introduction\n\nThe Flood mapping python toolbox (Floodpy) is a free and open-source python toolbox for mapping the non-urban flooded regions. It exploits the dense Sentinel-1 GRD intensity time series using a statistical or a ViT (Visual Transfomer) approach. Before running Floodpy make use you know the following information of the flood event of your interest\n - Date and time of the flood event\n - Spatial information (e.g. min,max latitude and min,max longitude) of the flood event\n\nThis is research code provided to you \"as is\" with NO WARRANTIES OF CORRECTNESS. Use at your own risk.\n\n\u003cimg src=\"https://github.com/kleok/FLOODPY/blob/main/figures/pinieios_results_github.png\" width=\"900\"\u003e\n\n## 1. Installation\n\nThe installation notes below are tested only on Linux. \nRecommended setup: Python 3.9+, SNAP 9.0+\n\n### 1.1 Install snap gpt including Sentinel-1 toolbox\n\nPlease download ESA-SNAP (All Toolboxes) from [here](https://step.esa.int/main/download/snap-download/) and install it using the following commands:\n\n  ```bash\n  chmod +x esa-snap_all_linux-10.0.0.sh\n  ./esa-snap_all_linux-10.0.0.sh\n  ```\n\n### 1.2 Account setup for downloading Sentinel-1 acquisitions\n\nEven though we offer credentials (for demonstration reasons), we encourage you\nto create your own account in order to not encounter any problems due to\ntraffic.\n\n- Please create an account at: [Copernicus-DataSpace](https://dataspace.copernicus.eu/).\n\n### 1.3 Account setup for downloading global atmospheric model data\n\nFloodPy can download meteorological data from based on ERA-5 data. \nYou have to create a new account [here](https://cds.climate.copernicus.eu/) if you don't own a user account yet. \nAfter the creation of your profile, you will find your Personal Access Token on your User profile page. \nCreate manually a ```.cdsapirc``` file  under your ```HOME``` directory with the following information:\n\n```\nurl: https://cds.climate.copernicus.eu/api\nkey: Your Personal Access Token\n```\n\n### 1.4 Download FLOODPY\n\nYou can download FLOODPY toolbox using the following command:\n```git clone https://github.com/kleok/FLOODPY.git```\n\n### 1.5 Create python environment for FLOODPY\n\nFLOODPY is written in Python3 and relies on several Python modules. We suggest to install them by using conda.\n\n- Using **conda**\nCreate a new conda environement with required packages using the the file [FLOODPY_gpu_env.yml](https://github.com/kleok/FLOODPY/blob/main/FLOODPY_gpu_env.yml).\n\n```\nconda env create -f path_to_FLOODPY/FLOODPY_gpu_env.yml\n```\n\n### 1.6 Set environmental variables (Optional)\n\nAppend to .bashrc file\n```\nexport FLOODPY_HOME= path_of_the_FLOODPY_folder\nexport PYTHONPATH=${PYTHONPATH}:${FLOODPY_HOME}\nexport PATH=${PATH}:${FLOODPY_HOME}/floodpy\n```\n\n## 2. Running FLOODPY\n\nFLOODPY generates a map with flooded regions based on Sentinel-1 GRD products and meteorological data. \nSentinel-1 orbits are downloaded using the [sentineleof](https://github.com/scottstanie/sentineleof)\nYou can run FLOODPY using the following jupyter notebooks as templates.\n\n- [Floodpy statistical approach](https://nbviewer.org/github/kleok/FLOODPY/blob/main/Floodpyapp_stat.ipynb)\n- [Floodpy deep learning approach](https://nbviewer.org/github/kleok/FLOODPY/blob/main/Floodpyapp_Vit.ipynb)\n\n## 3. Documentation and citation\nAlgorithms implemented in the software are described in detail at our publications. If FLOODPY was useful for you, we encourage you to cite the following work: \n- Karamvasis K, Karathanassi V. FLOMPY: An Open-Source Toolbox for Floodwater Mapping Using Sentinel-1 Intensity Time Series. Water. 2021; 13(21):2943. https://doi.org/10.3390/w13212943 \n\n- Kuro Siwo: 33 billion m2 under the water. A global multi-temporal satellite dataset for rapid flood mapping. https://paperswithcode.com/paper/kuro-siwo-12-1-billion-m-2-under-the-water-a\n\nYou can also have a look at other works that are using FLOODPY:\n\n- Gounari 0., Falagas A., Karamvasis K., Tsironis V., Karathanassi V., \nKarantzalos K.: Floodwater Mapping \u0026 Extraction of Flood-Affected \nAgricultural Fields. Living Planet Symposium Bonn 23-27 May 2022.      \nhttps://drive.google.com/file/d/1HiGkep3wx45gAQT6Kq34CdECMpQc8GUV/view?usp=sharing\n\n- Zotou I., Karamvasis K., Karathanassi V., Tsihrintzis V.: Sensitivity of a coupled 1D/2D \nmodel in input parameter variation exploiting Sentinel-1-derived flood map. \n7th IAHR Europe Congress. September 7-9, 2022. Page 247 at \nhttps://www.iahreuropecongress.org/PDF/IAHR2022_ABSTRACT_BOOK.pdf\n\n- Zotou I, Karamvasis K, Karathanassi V, Tsihrintzis VA. Potential of Two SAR-Based Flood Mapping Approaches in Supporting an Integrated 1D/2D HEC-RAS Model. Water. 2022; 14(24):4020. https://doi.org/10.3390/w14244020 \n\n## 4. Contact us\nFeel free to open an issue, comment or pull request. We would like to listen to your thoughts and your recommendations. 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