FLOODPY

Flood Python Toolbox

https://github.com/kleok/FLOODPY

Science Score: 26.0%

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    Low similarity (16.5%) to scientific vocabulary

Keywords

flood flood-monitoring floods python remote-sensing sentinel-1 surface-water synthetic-aperture-radar
Last synced: 6 months ago · JSON representation

Repository

Flood Python Toolbox

Basic Info
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  • Stars: 184
  • Watchers: 8
  • Forks: 33
  • Open Issues: 1
  • Releases: 4
Topics
flood flood-monitoring floods python remote-sensing sentinel-1 surface-water synthetic-aperture-radar
Created over 4 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

FLOODPY - FLOOD PYthon toolbox

GitHub license Release contributions welcome Documentation

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 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.

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 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 FLOODPYgpuenv.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 You can run FLOODPY using the following jupyter notebooks as templates.

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/IAHR2022ABSTRACTBOOK.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:

Owner

  • Name: Kleanthis Karamvasis
  • Login: kleok
  • Kind: user
  • Location: Athens, Greece
  • Company: National Technical University of Athens

InSAR enthusiast

GitHub Events

Total
  • Issues event: 1
  • Watch event: 13
  • Issue comment event: 1
  • Push event: 2
  • Pull request event: 5
  • Fork event: 4
Last Year
  • Issues event: 1
  • Watch event: 13
  • Issue comment event: 1
  • Push event: 2
  • Pull request event: 5
  • Fork event: 4

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 421
  • Total Committers: 9
  • Avg Commits per committer: 46.778
  • Development Distribution Score (DDS): 0.183
Past Year
  • Commits: 35
  • Committers: 3
  • Avg Commits per committer: 11.667
  • Development Distribution Score (DDS): 0.114
Top Committers
Name Email Commits
kleok k****k@h****m 344
alekfal a****s@g****m 50
Olyna o****g@l****m 15
Gounari Olympia 3****a 4
mkremezi m****i@g****m 3
mylonasma m****a@h****m 2
katadam a****n@g****m 1
Scott Staniewicz s****e@g****m 1
palexantonakis 4****s 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 35
  • Total pull requests: 57
  • Average time to close issues: 3 months
  • Average time to close pull requests: 1 day
  • Total issue authors: 14
  • Total pull request authors: 8
  • Average comments per issue: 2.63
  • Average comments per pull request: 0.05
  • Merged pull requests: 51
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 5
  • Pull requests: 11
  • Average time to close issues: 16 days
  • Average time to close pull requests: 3 days
  • Issue authors: 4
  • Pull request authors: 4
  • Average comments per issue: 2.0
  • Average comments per pull request: 0.18
  • Merged pull requests: 9
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • kleok (12)
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Pull Request Authors
  • kleok (46)
  • alekfal (10)
  • palexantonakis (5)
  • ckahootspammer (2)
  • mazingaro (2)
  • mkremezi (2)
  • mylonasma (1)
  • scottstanie (1)
Top Labels
Issue Labels
enhancement (6) development (4) documentation (2)
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Dependencies

docs/requirements-dev.txt pypi
  • awscli * development
  • cdsapi * development
  • folium * development
  • geopandas ==0.9.0 development
  • h5py * development
  • joblib * development
  • matplotlib * development
  • myst_parser * development
  • netCDF4 * development
  • numpy * development
  • pandas ==1.1.5 development
  • pyproj * development
  • rasterio * development
  • rasterstats * development
  • requests * development
  • richdem * development
  • scikit-image * development
  • scikit-learn * development
  • scipy * development
  • seaborn * development
  • sentinelsat * development
  • shapely * development
  • sphinx * development
  • sphinx_rtd_theme * development
  • sphinxcontrib-apidoc * development
  • sphinxcontrib-napoleon * development
  • tqdm * development
  • wheel * development