ml4floods

An ecosystem of data, models and code pipelines to tackle flooding with ML🌊

https://github.com/spaceml-org/ml4floods

Science Score: 59.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 6 DOI reference(s) in README
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Academic publication links
Links to: nature.com
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Committers with academic emails
3 of 19 committers (15.8%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (10.9%) to scientific vocabulary

Last synced: 6 months ago · JSON representation

Repository

An ecosystem of data, models and code pipelines to tackle flooding with ML🌊

Basic Info

Host: GitHub
Owner: spaceml-org
License: lgpl-3.0
Language: Jupyter Notebook
Default Branch: main
Homepage: https://spaceml-org.github.io/ml4floods/
Size: 448 MB

Statistics

Stars: 164
Watchers: 18
Forks: 42
Open Issues: 0
Releases: 0

Created about 5 years ago · Last pushed 7 months ago

Metadata Files

Readme Contributing License

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.

awesome flood extent estimation

Install

Install from pip:

bash pip install ml4floods

Install the latest version from GitHub:

bash 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

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 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 = {Portals-Juli, Enrique and Mateo-Garca, Gonzalo and Purcell, Cormac and Gmez-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, Atlm Gne 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. In addition, this research has been partially supported by the DEEPCLOUD project (PID2019-109026RB-I00) funded by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) and the European Union (NextGenerationEU).

Owner

Name: SpaceML
Login: spaceml-org
Kind: organization

Website: spaceml.org
Repositories: 19
Profile: https://github.com/spaceml-org

GitHub Events

Total

Watch event: 30
Push event: 5
Fork event: 1

Last Year

Watch event: 30
Push event: 5
Fork event: 1

Committers

Last synced: 7 months ago

All Time

Total Commits: 707
Total Committers: 19
Avg Commits per committer: 37.211
Development Distribution Score (DDS): 0.447

Past Year

Commits: 16
Committers: 4
Avg Commits per committer: 4.0
Development Distribution Score (DDS): 0.5

Top Committers

Name	Email	Commits
Gonzalo Mateo	g**8@g**m	391
Emmanuel Johnson	e**1@g**m	96
Gonzalo Mateo Garcia	g**a@u**g	40
Satyarth Praveen	s**4@g**m	32
Kike	s**s@g**m	29
Nicholas Roth	n**s@r**t	23
nadia-eecs	a**n@d**l	23
Sam Budd	b**l@g**m	22
Lucas Kruitwagen	l**n@g**m	21
nadia-eecs	a**n@u**u	11
Margaret Maynard-Reid	m****z	5
Kike Portales	k**e@u**l	5
crpurcell	c**l@g**m	2
Nadia Ahmed	a**n@t**u	2
Tommy Lees	t**2@g**m	1
Satyarth Praveen	s**4@d**l	1
Samuel Budd	s**3@i**k	1
Kike Portales	k**e@d**s	1
kgupta	6****9	1

Committer Domains (Top 20 + Academic)

deep.uv.es: 1 imperial.ac.uk: 1 data-janitors-v2.us-east1-b.c.ml4cc-general.internal: 1 tupa.ps.uci.edu: 1 uci.edu: 1 data-janitors.us-east1-b.c.ml4cc-general.internal: 1 rothemail.net: 1 un.org: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 38
Total pull requests: 73
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 requests: 66
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

jejjohnson (21)
gonzmg88 (7)
R-Strange (3)
rothn (2)
nadia-eecs (1)
kgupta359 (1)
tommylees112 (1)
Lkruitwagen (1)

Pull Request Authors

gonzmg88 (30)
jejjohnson (17)
rothn (5)
kipoju (4)
Lkruitwagen (4)
margaretmz (3)
sambuddinc (2)
nadia-eecs (2)
nkasmanoff (1)
Qaraqororum (1)
tarunn2799 (1)
AleksandrTulenkov (1)
tommylees112 (1)
crpurcell (1)

Top Labels

Issue Labels

dataprep (24) enhancement (16) models (7) help wanted (4) documentation (3) good first issue (1)

Pull Request Labels

dataprep (8) enhancement (6) documentation (2) good first issue (1) models (1) bug (1)

Packages

Total packages: 1
Total downloads:
- pypi 149 last-month

Total dependent packages: 0
Total dependent repositories: 1
Total versions: 6
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/
License: GNU Lesser General Public License v3 (LGPLv3)
Latest release: 1.1.1
published 7 months ago

Versions: 6
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 149 Last month

Rankings

Dependent packages count: 10.1%

Dependent repos count: 21.6%

Average: 26.2%

Downloads: 47.0%

Maintainers (2)

gonzmg88 kipoju

Last synced: 6 months ago

Dependencies

jupyterbook/requirements.txt pypi

ghp-import *
jupyter-book *
matplotlib *
numpy *

.github/workflows/deploy.yml actions

actions/checkout v2 composite
actions/setup-python v1 composite
peaceiris/actions-gh-pages v3.6.1 composite

requirements.txt pypi

albumentations *
earthengine-api *
fsspec *
gcsfs *
geopandas *
google-cloud-storage *
matplotlib *
matplotlib-scalebar *
mercantile *
numpy *
pandas *
pytorch-lightning *
rasterio *
requests_html *
seaborn *
torch *
torchvision *
tqdm *

setup.py pypi

ml4floods

Science Score: 59.0%

Repository

Basic Info

Statistics

Metadata Files

README.md

Install

Docs

The WorldFloods database

Cite

About

Owner

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: ml4floods

Rankings

Maintainers (2)

Dependencies