worldcereal-classification

This repository contains the classification module of the WorldCereal system.

https://github.com/worldcereal/worldcereal-classification

Keywords from Contributors

earth-observation openeo

Last synced: 6 months ago · JSON representation

Repository

This repository contains the classification module of the WorldCereal system.

Basic Info

Host: GitHub
Owner: WorldCereal
License: mit
Language: Python
Default Branch: main
Homepage: https://esa-worldcereal.org/
Size: 202 MB

Statistics

Stars: 56
Watchers: 6
Forks: 8
Open Issues: 45
Releases: 8

Created almost 3 years ago · Last pushed 6 months ago

Metadata Files

Readme License

ESA WorldCereal classification module

Overview

WorldCereal is a Python package designed for generating cropland and crop type maps at a wide range of spatial scales, leveraging satellite and auxiliary data, and state-of-the-art classification workflows. It uses openEO to run classification tasks in the cloud, by default the Copernicus Data Space Ecosystem (CDSE).

Users can leverage the system in a notebook environment through Terrascope or set up the environment locally using the provided installation options.

In order to run classification jobs on CDSE, users can get started with monthly free openEO processing credits by registering on the CDSE platform. Additional credits can be purchased, or users may soon be able to request them through the ESA Network of Resources.

Features

Scalable: Generate maps at a wide range of spatial scales.
Cloud-based Processing: Leverages openEO to run classifications in the cloud.
Powerful classification pipeline: WorldCereal builds upon Presto, a pretrained transformer-based model, leveraging global self-supervised learning of multimodal input timeseries, leading to better accuracies and higher generalizability in space and time of downstream crop classification models. The Presto backbone of WorldCereal classification pipelines is developed here.
Customizable: Users can pick any region or temporal range and apply either default models or train their own and produce custom maps, interacting with publicly available training data.
Easy to Use: Integrates into Jupyter notebooks and other Python environments.

Quick Start

One of our demo notebooks can introduce to you quickly how to map crops with the Worldcereal system. There's two options to run these notebooks:

Option 1: Run on Terrascope

You can use a preconfigured environment on Terrascope to run the workflows in a Jupyter notebook environment. Just register as a new user on Terrascope or use one of the supported EGI eduGAIN login methods to get started.

| :point_up: | Once you are prompted with "Server Options", make sure to select the "Worldcereal" image. Did you choose "Terrascope" by accident? Then go to File > Hub Control Panel > Stop my server, and click the link below once again. | |---------------|:------------------------|

For a cropland map generation demo without any model training:
For a crop type map generation demo with model training:
For a demo on how to interact with the WorldCereal Reference Data Module (RDM):

Option 2: Install Locally

If you prefer to install the package locally, you can create the environment using Conda or pip.

First clone the repository: bash git clone https://github.com/WorldCereal/worldcereal-classification.git cd worldcereal-classification Next, install the package locally: - for Conda: conda env create -f environment.yml - for Pip: pip install .[train,notebooks]

Usage Example

In its most simple form, a cropland mask can be generated with just few lines of code, triggering an openEO job on CDSE and downloading the result locally:

```python from openeogfmap import BoundingBoxExtent, TemporalContext from worldcereal.job import generatemap

Specify the spatial extent

spatial_extent = BoundingBoxExtent( west=44.432274, south=51.317362, east=44.698802, north=51.428224, epsg=4326 )

Specify the temporal extent (this has to be one year)

temporal_extent = TemporalContext('2022-11-01', '2023-10-31')

Launch processing job (result will automatically be downloaded)

results = generatemap(spatialextent, temporalextent, outputdir='.') ```

Documentation

Comprehensive documentation is available at the following link: https://worldcereal.github.io/worldcereal-documentation/

Support

Questions, suggestions, feedback? Use our forum to get in touch with us and the rest of the community!

License

This project is licensed under the terms of the MIT License. See the LICENSE file for details.

Acknowledgments

The WorldCereal project is funded by the European Space Agency (ESA) under grant no. 4000130569/20/I-NB.

WorldCereal's classification backbone makes use of the Presto model, originally implemented here. Without the groundbreaking work being done by Gabriel Tseng and the rest of the NASA Harvest team, both in the original Presto implementation as well as its adaptation for WorldCereal, this package would simply not exist in its present form 🙏.

The pre-configured Jupyter notebook environment in which users can train custom models and launch WorldCereal jobs is provided by Terrascope, the Belgian Earth observation data space, managed by VITO Remote Sensing on behalf of the Belgian Science Policy Office

How to cite

If you use WorldCereal resources in your work, please cite it as follows:

```bibtex

@article{vantrichtworldcereal_2023, title = {{WorldCereal}: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping}, volume = {15}, issn = {1866-3516}, shorttitle = {{WorldCereal}}, url = {https://essd.copernicus.org/articles/15/5491/2023/}, doi = {10.5194/essd-15-5491-2023}, number = {12}, urldate = {2024-03-01}, journal = {Earth System Science Data}, author = {Van Tricht, Kristof and Degerickx, Jeroen and Gilliams, Sven and Zanaga, Daniele and Battude, Marjorie and Grosu, Alex and Brombacher, Joost and Lesiv, Myroslava and Bayas, Juan Carlos Laso and Karanam, Santosh and Fritz, Steffen and Becker-Reshef, Inbal and Franch, Belén and Mollà-Bononad, Bertran and Boogaard, Hendrik and Pratihast, Arun Kumar and Koetz, Benjamin and Szantoi, Zoltan}, month = dec, year = {2023}, pages = {5491--5515}, } ```

Owner

Name: ESA WorldCereal
Login: WorldCereal
Kind: organization

Website: https://esa-worldcereal.org/
Repositories: 2
Profile: https://github.com/WorldCereal

The WorldCereal consortium has been funded by ESA (European Space Agency) to perform global crop mapping at field scale

GitHub Events

Total

Create event: 101
Commit comment event: 1
Release event: 7
Issues event: 176
Watch event: 31
Delete event: 90
Issue comment event: 236
Push event: 517
Pull request review comment event: 294
Pull request review event: 315
Pull request event: 176
Fork event: 6

Last Year

Create event: 101
Commit comment event: 1
Release event: 7
Issues event: 176
Watch event: 31
Delete event: 90
Issue comment event: 237
Push event: 517
Pull request review comment event: 294
Pull request review event: 315
Pull request event: 176
Fork event: 6

Committers

Last synced: 6 months ago

All Time

Total Commits: 1,422
Total Committers: 14
Avg Commits per committer: 101.571
Development Distribution Score (DDS): 0.547

Past Year

Commits: 1,083
Committers: 10
Avg Commits per committer: 108.3
Development Distribution Score (DDS): 0.576

Top Committers

Name	Email	Commits
Kristof Van Tricht	k**t@v**e	644
jdegerickx	j**x@v**e	404
Vincent Verelst	v**t@v**e	126
Darius Couchard	d**d@v**e	108
Butsko Christina	b**c@s**l	38
Christina Butsko	c**o@c**e	37
Christina Butsko	c**o@c**e	31
Hans Vanrompay	h**y@h**m	11
Vincent Verelst	v**t@v**e	9
Butsko Christina	b**c@s**l	6
giollimirgia	m**a@g**m	5
Stefaan Lippens	s**s@v**e	1
Jeroen Dries	j**s@v**e	1
World Cereal	w**s@c**e	1

Committer Domains (Top 20 + Academic)

vito.be: 6 couchard-wc-extractions.vitovm.rscluster.vito.be: 1 vverelst.vitovm.rscluster.vito.be: 1 cbutsko.vitovm.rscloud.vito.be: 1 cbutsko.vitovm.rscluster.vito.be: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 215
Total pull requests: 339
Average time to close issues: about 2 months
Average time to close pull requests: 5 days
Total issue authors: 18
Total pull request authors: 8
Average comments per issue: 1.34
Average comments per pull request: 0.53
Merged pull requests: 275
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 127
Pull requests: 243
Average time to close issues: 29 days
Average time to close pull requests: 5 days
Issue authors: 14
Pull request authors: 6
Average comments per issue: 0.7
Average comments per pull request: 0.44
Merged pull requests: 200
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

kvantricht (106)
VincentVerelst (26)
cbutsko (25)
jdegerickx (16)
GriffinBabe (13)
jdries (11)
shabarinath4 (5)
HansVRP (2)
santoshkaranam (2)
Rupam-kumawat (1)
CButsko (1)
MolettoLobos (1)
tomaszps (1)
mal147 (1)
emmamusiari (1)

Pull Request Authors

kvantricht (159)
jdegerickx (92)
VincentVerelst (39)
GriffinBabe (33)
cbutsko (11)
soxofaan (2)
giollimirgia (2)
jdries (1)

Top Labels

Issue Labels

enhancement (12) bug (10) deliverable (1) documentation (1) Epic (1) Presto (1)

Pull Request Labels

codex (6) bug (2) enhancement (2)

Packages

Total packages: 2
Total downloads: unknown

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 0
(may contain duplicates)
Total versions: 18

proxy.golang.org: github.com/worldcereal/worldcereal-classification

Documentation: https://pkg.go.dev/github.com/worldcereal/worldcereal-classification#section-documentation
License: mit
Latest release: v2.4.1+incompatible
published 7 months ago

Versions: 9
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 6 months ago

proxy.golang.org: github.com/WorldCereal/worldcereal-classification

Documentation: https://pkg.go.dev/github.com/WorldCereal/worldcereal-classification#section-documentation
License: mit
Latest release: v2.4.1+incompatible
published 7 months ago

Versions: 9
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 6 months ago

worldcereal-classification

Science Score: 49.0%

Keywords from Contributors

Repository

Basic Info

Statistics

Metadata Files

README.md

ESA WorldCereal classification module

Overview

Features

Quick Start

Option 1: Run on Terrascope

Option 2: Install Locally

Usage Example

Specify the spatial extent

Specify the temporal extent (this has to be one year)

Launch processing job (result will automatically be downloaded)

Documentation

Support

License

Acknowledgments

How to cite

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

proxy.golang.org: github.com/worldcereal/worldcereal-classification

Rankings

proxy.golang.org: github.com/WorldCereal/worldcereal-classification

Rankings