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https://github.com/kleok/FLOODPY
Flood Python Toolbox
https://github.com/kleok/FLOODPY
flood flood-monitoring floods python remote-sensing sentinel-1 surface-water synthetic-aperture-radar
Last synced: 26 days ago
JSON representation
Flood Python Toolbox
- Host: GitHub
- URL: https://github.com/kleok/FLOODPY
- Owner: kleok
- License: other
- Created: 2021-06-21T16:42:00.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-10-26T21:06:07.000Z (about 2 months ago)
- Last Synced: 2024-10-29T19:48:55.142Z (about 2 months ago)
- Topics: flood, flood-monitoring, floods, python, remote-sensing, sentinel-1, surface-water, synthetic-aperture-radar
- Language: Jupyter Notebook
- Homepage: https://floodpy.readthedocs.io/en/latest/
- Size: 287 MB
- Stars: 170
- Watchers: 8
- Forks: 32
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- open-sustainable-technology - FLOODPY - A free and open-source python toolbox for mapping of floodwater. (Climate Change / Natural Hazard and Storm)
README
# 
FLOODPY - FLOOD PYthon toolbox
[](https://github.com/kleok/FLOODPY)
[](https://github.com/kleok/FLOODPY)
[](https://github.com/kleok/FLOODPY/issues)
[](https://floodpy.readthedocs.io/en/latest/)
## Introduction
The 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
- Date and time of the flood event
- Spatial information (e.g. min,max latitude and min,max longitude) of the flood event
This is research code provided to you "as is" with NO WARRANTIES OF CORRECTNESS. Use at your own risk.
## 1. Installation
The installation notes below are tested only on Linux.
Recommended setup: Python 3.9+, SNAP 9.0+
### 1.1 Install snap gpt including Sentinel-1 toolbox
Please download ESA-SNAP (All Toolboxes) from [here](https://step.esa.int/main/download/snap-download/) and install it using the following commands:
```bash
chmod +x esa-snap_all_linux-10.0.0.sh
./esa-snap_all_linux-10.0.0.sh
```
### 1.2 Account setup for downloading Sentinel-1 acquisitions
Even though we offer credentials (for demonstration reasons), we encourage you
to create your own account in order to not encounter any problems due to
traffic.
- Please create an account at: [Copernicus-DataSpace](https://dataspace.copernicus.eu/).
### 1.3 Account setup for downloading global atmospheric model data
FloodPy can download meteorological data from based on ERA-5 data.
You have to create a new account [here](https://cds.climate.copernicus.eu/) if you don't own a user account yet.
After the creation of your profile, you will find your Personal Access Token on your User profile page.
Create manually a ```.cdsapirc``` file under your ```HOME``` directory with the following information:
```
url: https://cds.climate.copernicus.eu/api
key: Your Personal Access Token
```
### 1.4 Download FLOODPY
You can download FLOODPY toolbox using the following command:
```git clone https://github.com/kleok/FLOODPY.git```
### 1.5 Create python environment for FLOODPY
FLOODPY is written in Python3 and relies on several Python modules. We suggest to install them by using conda.
- Using **conda**
Create 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).
```
conda env create -f path_to_FLOODPY/FLOODPY_gpu_env.yml
```
### 1.6 Set environmental variables (Optional)
Append to .bashrc file
```
export FLOODPY_HOME= path_of_the_FLOODPY_folder
export PYTHONPATH=${PYTHONPATH}:${FLOODPY_HOME}
export PATH=${PATH}:${FLOODPY_HOME}/floodpy
```
## 2. Running FLOODPY
FLOODPY generates a map with flooded regions based on Sentinel-1 GRD products and meteorological data.
Sentinel-1 orbits are downloaded using the [sentineleof](https://github.com/scottstanie/sentineleof)
You can run FLOODPY using the following jupyter notebooks as templates.
- [Floodpy statistical approach](https://nbviewer.org/github/kleok/FLOODPY/blob/main/Floodpyapp_stat.ipynb)
- [Floodpy deep learning approach](https://nbviewer.org/github/kleok/FLOODPY/blob/main/Floodpyapp_Vit.ipynb)
## 3. Documentation and citation
Algorithms 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:
- 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
- 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
You can also have a look at other works that are using FLOODPY:
- Gounari 0., Falagas A., Karamvasis K., Tsironis V., Karathanassi V.,
Karantzalos K.: Floodwater Mapping & Extraction of Flood-Affected
Agricultural Fields. Living Planet Symposium Bonn 23-27 May 2022.
https://drive.google.com/file/d/1HiGkep3wx45gAQT6Kq34CdECMpQc8GUV/view?usp=sharing
- Zotou I., Karamvasis K., Karathanassi V., Tsihrintzis V.: Sensitivity of a coupled 1D/2D
model in input parameter variation exploiting Sentinel-1-derived flood map.
7th IAHR Europe Congress. September 7-9, 2022. Page 247 at
https://www.iahreuropecongress.org/PDF/IAHR2022_ABSTRACT_BOOK.pdf
- 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
## 4. Contact us
Feel free to open an issue, comment or pull request. We would like to listen to your thoughts and your recommendations. Any help is very welcome! :heart: