ML4Floods
An ecosystem of data, models and code pipelines to tackle flooding with machine learning.
https://github.com/spaceml-org/ml4floods
Category: Climate Change
Sub Category: Natural Hazard and Storm
Last synced: about 24 hours ago
JSON representation
Repository metadata
An ecosystem of data, models and code pipelines to tackle flooding with ML🌊
- Host: GitHub
- URL: https://github.com/spaceml-org/ml4floods
- Owner: spaceml-org
- License: lgpl-3.0
- Created: 2021-02-03T16:50:16.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-10-05T15:47:44.000Z (7 months ago)
- Last Synced: 2025-04-25T14:07:59.158Z (2 days ago)
- Language: Jupyter Notebook
- Homepage: https://spaceml-org.github.io/ml4floods/
- Size: 500 MB
- Stars: 151
- Watchers: 16
- Forks: 41
- Open Issues: 0
- Releases: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
README.md
ML4Floods is an end-to-end ML pipeline for flood extent estimation: from data preprocessing, model training, model deployment to visualization. Here you can find the WorldFloodsV2🌊 dataset and trained models 🤗 for flood extent estimation in Sentinel-2 and Landsat.
Install
Install from pip:
pip install ml4floods
Install the latest version from GitHub:
pip install git+https://github.com/spaceml-org/ml4floods#egg=ml4floods
Docs
These tutorials may help you explore the datasets and models:
- Kherson Dam Break end-to-end flood extent map
- Run the model on time series of Sentinel-2 images
- Ingest data from Copernicus EMS
- ML-models step by step
- Training
- Inference on new data (a Sentinel-2 image)
- Perf metrics
- Training
The WorldFloods database
The WorldFloods database contains 509 pairs of Sentinel-2 images and flood segmentation masks.
It requires approximately 76GB of hard-disk storage.
The WorldFloods database and all pre-trained models are released under a Creative Commons non-commercial licence
To download the WorldFloods database or the pretrained flood segmentation models see the instructions to download the database.
Cite
If you find this work useful please cite:
@article{portales-julia_global_2023,
title = {Global flood extent segmentation in optical satellite images},
volume = {13},
issn = {2045-2322},
doi = {10.1038/s41598-023-47595-7},
number = {1},
urldate = {2023-11-30},
journal = {Scientific Reports},
author = {Portalés-Julià , Enrique and Mateo-GarcÃa, Gonzalo and Purcell, Cormac and Gómez-Chova, Luis},
month = nov,
year = {2023},
pages = {20316},
}
@article{mateo-garcia_towards_2021,
title = {Towards global flood mapping onboard low cost satellites with machine learning},
volume = {11},
issn = {2045-2322},
doi = {10.1038/s41598-021-86650-z},
number = {1},
urldate = {2021-04-01},
journal = {Scientific Reports},
author = {Mateo-Garcia, Gonzalo and Veitch-Michaelis, Joshua and Smith, Lewis and Oprea, Silviu Vlad and Schumann, Guy and Gal, Yarin and Baydin, Atılım Güneş and Backes, Dietmar},
month = mar,
year = {2021},
pages = {7249},
}
About
ML4Floods has been funded by the United Kingdom Space Agency (UKSA) and led by Trillium Technologies. It has also been partially supported by the Spanish Ministry of Science and Innovation project PID2019-109026RB-I00 (MINECO-ERDF MCIN/AEI/10.13039/501100011033).
Owner metadata
- Name: SpaceML
- Login: spaceml-org
- Email:
- Kind: organization
- Description:
- Website: spaceml.org
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/75035593?v=4
- Repositories: 19
- Last ynced at: 2023-03-08T22:00:19.530Z
- Profile URL: https://github.com/spaceml-org
GitHub Events
Total
- Watch event: 19
- Fork event: 1
Last Year
- Watch event: 19
- Fork event: 1
Committers metadata
Last synced: 7 days ago
Total Commits: 695
Total Committers: 18
Avg Commits per committer: 38.611
Development Distribution Score (DDS): 0.443
Commits in past year: 32
Committers in past year: 3
Avg Commits per committer in past year: 10.667
Development Distribution Score (DDS) in past year: 0.438
| Name | Commits | |
|---|---|---|
| Gonzalo Mateo | g****8@g****m | 387 |
| Emmanuel Johnson | e****1@g****m | 96 |
| Gonzalo Mateo Garcia | g****a@u****g | 34 |
| Satyarth Praveen | s****4@g****m | 32 |
| Kike | s****s@g****m | 28 |
| nadia-eecs | a****n@d****l | 23 |
| Nicholas Roth | n****s@r****t | 23 |
| Sam Budd | b****l@g****m | 22 |
| Lucas Kruitwagen | l****n@g****m | 21 |
| nadia-eecs | a****n@u****u | 11 |
| Kike Portales | k****e@u****l | 5 |
| Margaret Maynard-Reid | m****z | 5 |
| crpurcell | c****l@g****m | 2 |
| Nadia Ahmed | a****n@t****u | 2 |
| Tommy Lees | t****2@g****m | 1 |
| kgupta | 6****9 | 1 |
| Samuel Budd | s****3@i****k | 1 |
| Satyarth Praveen | s****4@d****l | 1 |
Committer domains:
- data-janitors-v2.us-east1-b.c.ml4cc-general.internal: 1
- imperial.ac.uk: 1
- tupa.ps.uci.edu: 1
- uci.edu: 1
- rothemail.net: 1
- data-janitors.us-east1-b.c.ml4cc-general.internal: 1
- un.org: 1
Issue and Pull Request metadata
Last synced: 2 days ago
Total issues: 38
Total pull requests: 74
Average time to close issues: about 1 year
Average time to close pull requests: 8 days
Total issue authors: 9
Total pull request authors: 15
Average comments per issue: 0.92
Average comments per pull request: 0.22
Merged pull request: 66
Bot issues: 0
Bot pull requests: 0
Past year issues: 0
Past year pull requests: 0
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
Past year issue authors: 0
Past year pull request authors: 0
Past year average comments per issue: 0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- jejjohnson (21)
- gonzmg88 (7)
- R-Strange (3)
- rothn (2)
- nadia-eecs (1)
- kgupta359 (1)
- flamboyant-vinci (1)
- tommylees112 (1)
- Lkruitwagen (1)
Top Pull Request Authors
- gonzmg88 (30)
- jejjohnson (17)
- rothn (5)
- kipoju (4)
- Lkruitwagen (4)
- margaretmz (3)
- sambuddinc (2)
- nadia-eecs (2)
- Qaraqororum (1)
- tarunn2799 (1)
- kgupta359 (1)
- AleksandrTulenkov (1)
- nkasmanoff (1)
- tommylees112 (1)
- crpurcell (1)
Top Issue Labels
- dataprep (24)
- enhancement (16)
- models (7)
- help wanted (4)
- documentation (3)
- good first issue (1)
Top Pull Request Labels
- dataprep (8)
- enhancement (6)
- documentation (2)
- good first issue (1)
- models (1)
- bug (1)
Package metadata
- Total packages: 1
-
Total downloads:
- pypi: 275 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 4
- Total maintainers: 2
pypi.org: ml4floods
Machine learning models for end-to-end flood extent segmentation.
- Homepage: https://github.com/spaceml-org/ml4floods
- Documentation: https://ml4floods.readthedocs.io/
- Licenses: GNU Lesser General Public License v3 (LGPLv3)
- Latest release: 1.0.1 (published over 1 year ago)
- Last Synced: 2025-04-25T14:05:14.926Z (2 days ago)
- Versions: 4
- Dependent Packages: 0
- Dependent Repositories: 1
- Downloads: 275 Last month
-
Rankings:
- Dependent packages count: 10.082%
- Dependent repos count: 21.62%
- Average: 26.241%
- Downloads: 47.021%
- Maintainers (2)
Dependencies
- ghp-import *
- jupyter-book *
- matplotlib *
- numpy *
- actions/checkout v2 composite
- actions/setup-python v1 composite
- peaceiris/actions-gh-pages v3.6.1 composite
- albumentations *
- earthengine-api *
- fsspec *
- gcsfs *
- geopandas *
- google-cloud-storage *
- matplotlib *
- matplotlib-scalebar *
- mercantile *
- numpy *
- pandas *
- pytorch-lightning *
- rasterio *
- requests_html *
- seaborn *
- torch *
- torchvision *
- tqdm *
Score: 13.535272708401726