BreizhCrops
A Satellite Time Series Dataset for Crop Type Identification.
https://github.com/dl4sits/BreizhCrops
Category: Consumption
Sub Category: Agriculture and Nutrition
Last synced: about 13 hours ago
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Repository metadata
A Satellite Time Series Dataset for Crop Type Identification
- Host: GitHub
- URL: https://github.com/dl4sits/BreizhCrops
- Owner: dl4sits
- License: gpl-3.0
- Created: 2019-04-27T12:30:07.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-05-11T10:53:26.000Z (almost 3 years ago)
- Last Synced: 2025-04-17T22:43:07.458Z (10 days ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 20.4 MB
- Stars: 203
- Watchers: 7
- Forks: 44
- Open Issues: 15
- Releases: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
readme.md
BreizhCrops:
A Time Series Dataset for Crop Type Mapping
Check our Breizhcrops Tutorial Colab Notebook for quick hands-on examples.
Installation
Linux and macOS
Install Breizhcrops as python package from PyPI!
pip install breizhcrops
Windows
If you use Windows, execute these lines.
git clone https://github.com/dl4sits/BreizhCrops.git
pip install torch==1.6.0 -f https://download.pytorch.org/whl/torch_stable.html
conda install gdal fiona geopandas
pip install .
Getting Started
This minimal working example
# import package
import breizhcrops as bzh
# initialize and download FRH04 data
dataset = bzh.BreizhCrops("frh04")
# get data sample
x, y, field_id = dataset[0]
# load pretrained model
model = bzh.models.pretrained("Transformer")
# create a batch of batchsize 1
x = x.unsqueeze(0)
# perform inference
y_pred = model(x)
downloads the FRH04 dataset partition (used for evaluation), loads a pretrained model and performs a prediction on the first sample.
Furthermore, for a detailed data analysis you can check the Hands-on Tutorial on Time Series. This is a Jupyter Notebook for timeseries data exploration with BreizhCrops benchmark.
Train a model
Train a model via the example script train.py
python train.py TransformerEncoder --learning-rate 0.001 --weight-decay 5e-08 --preload-ram
This script uses the default model parameters from breizhcrops.models.TransformerModel.
When training multiple epochs, the --preload-ram flag speeds up training significantly
Acknowledgements
The model implementations from this repository are based on the following papers and github repositories.
- TempCNN (reimplementation from keras source code ) Pelletier et al., 2019
- LSTM Recurrent Neural Network adapted from Rußwurm & Körner, 2017
- MS-ResNet implementation from Fei Wang
- TransformerEncoder implementation was originally adopted from Yu-Hsiang Huang GitHub, but later replaced by own implementation when
torch.nn.transformermodules became available - InceptionTime Fawaz et al., 2019
- StarRNN Turkoglu et al., 2019
- OmniscaleCNN Tang et al., 2020
The raw label data originates from
- Registre parcellaire graphique (RPG) of the French National Geographic Institute (IGN)
Reference
This work will be published in the proceedings of ISPRS Archives 2020. Preprint available on ArXiv
@article{breizhcrops2020,
title={BreizhCrops: A Time Series Dataset for Crop Type Mapping},
author={Ru{\ss}wurm, Marc and Pelletier, Charlotte and Zollner, Maximilian and Lef{\`e}vre, S{\'e}bastien and K{\"o}rner, Marco},
journal={International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS (2020)},
year={2020}
}
ISPRS virtual congress video can be found here
ICML workshop 2019
A previous version (see workshop website or arxiv version 1) was presented at the
presented at the ICML 2019 Time Series workshop, Long Beach, USA
ICML workshop contributions do not appear in the ICML proceedings.
Owner metadata
- Name: dl4sits
- Login: dl4sits
- Email:
- Kind: organization
- Description:
- Website:
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- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/58819454?v=4
- Repositories: 2
- Last ynced at: 2023-03-06T22:17:35.507Z
- Profile URL: https://github.com/dl4sits
GitHub Events
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- Watch event: 9
- Fork event: 2
Last Year
- Watch event: 9
- Fork event: 2
Committers metadata
Last synced: 8 days ago
Total Commits: 217
Total Committers: 4
Avg Commits per committer: 54.25
Development Distribution Score (DDS): 0.244
Commits in past year: 0
Committers in past year: 0
Avg Commits per committer in past year: 0.0
Development Distribution Score (DDS) in past year: 0.0
| Name | Commits | |
|---|---|---|
| Marc Rußwurm | m****m@t****e | 164 |
| Maximilian Zollner | m****r@t****e | 44 |
| Charlotte P | c****r@u****r | 8 |
| Marco Körner | m****r@t****e | 1 |
Committer domains:
- tum.de: 3
- univ-ubs.fr: 1
Issue and Pull Request metadata
Last synced: 1 day ago
Total issues: 35
Total pull requests: 4
Average time to close issues: 19 days
Average time to close pull requests: about 9 hours
Total issue authors: 14
Total pull request authors: 3
Average comments per issue: 0.97
Average comments per pull request: 0.25
Merged pull request: 4
Bot issues: 0
Bot pull requests: 0
Past year issues: 2
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: 1
Past year pull request authors: 0
Past year average comments per issue: 0.0
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
- MarcCoru (15)
- charlotte-pel (4)
- Luffy2Github (3)
- JunwenBai (2)
- DivinorWieldor (2)
- gsp2188 (1)
- burcsuslu (1)
- stienheremans (1)
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Top Pull Request Authors
- maxzoll (2)
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Top Issue Labels
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Package metadata
- Total packages: 1
-
Total downloads:
- pypi: 501 last-month
- Total dependent packages: 0
- Total dependent repositories: 4
- Total versions: 14
- Total maintainers: 1
pypi.org: breizhcrops
A Satellite Time Series Dataset for Crop Type Identification
- Homepage: http://github.com/dl4sits/breizhcrops
- Documentation: https://breizhcrops.readthedocs.io/
- Licenses: MIT
- Latest release: 0.0.4.1 (published almost 3 years ago)
- Last Synced: 2025-04-26T13:31:58.111Z (1 day ago)
- Versions: 14
- Dependent Packages: 0
- Dependent Repositories: 4
- Downloads: 501 Last month
-
Rankings:
- Stargazers count: 5.556%
- Forks count: 6.708%
- Dependent packages count: 7.31%
- Dependent repos count: 7.649%
- Average: 10.905%
- Downloads: 27.304%
- Maintainers (1)
Dependencies
- geojson >=2.4.1
- geopandas >=0.5.0
- h5py *
- jupyter >=1.0.0
- matplotlib >=3.1.0
- numpy *
- pandas >=0.24.2
- pytest *
- requests *
- scikit-learn *
- seaborn >=0.9.0
- torch >=1.6.0
- tqdm >=4.32.2
Score: 12.997326093196445