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Sen4AgriNet: A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning

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Orion-AI-Lab/S4A

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A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning

Contributors:Sykas D.,Zografakis D.,Sdraka M.

Supplementary repo with DL experiments using the Sen4AgriNet dataset:Sen4AgriNet-Models.

This repository provides a native PyTorch Dataset Class for Sen4AgriNet dataset (patches_dataset.py). Should work with any new version of PyTorch1.7.1+ and Python3.8.5+.

Dataset heavily relies oncocoapi for dataloading and indexing, therefore make sure you have it installed:

pip3installpycocotools

Then make sure every other requirement is installed:

pip3install-rrequirements.txt

Instructions

In order to use the provided PyTroch Dataset class, the required netCDF files of Sen4AgriNet must be downloaded and placed inside thedataset/netcdf/ folder. These files are available for download atDropbox,Google Drive andHuggingFace Hub.

Then, three separate COCO files must be created: one for training, one for validation and one for testing. Alternatively, the predefined COCO files for the 3 Scenarios can be downloaded fromhere.

After this initial setup,patches_dataset.py can be used in a PyTorch deep learning pipeline to load, prepare and return patches from the dataset according to the split dictated by the COCO files. This Dataset class has the following features:

  • Reads the netCDF files of the dataset containing the Sentinel-2 observations over time and the corresponding labels.
  • Isolates the Sentinel-2 bands requested by the user.
  • Computes the median Sentinel-2 image on a given frequency, e.g. monthly (or loads precomputed medians, if any).
  • Returns the timeseries of median images inside a predefined window.
  • Normalizes the images.
  • Returns hollstein masks for clouds, cirrus, shadow or snow.
  • Returns a parcel mask: 1 for parcel, 0 for non-parcel.
  • Can alternatively return binary labels: 1 for crops, 0 for non-crops.

Dataset exploration

This is roughly the way that ourpatches_dataset.py works. The whole procedure is also described in the providednotebook.

  1. Open a netCDF file for exploration.
importnetCDF4frompathlibimportPathpatch=netCDF4.Dataset(Path('data/2020_31TCG_patch_14_14.nc'),'r')patch

Outputs

"""<class 'netCDF4._netCDF4.Dataset'>root group (NETCDF4 data model, file format HDF5):    title: S4A Patch Dataset    authors: Papoutsis I., Sykas D., Zografakis D., Sdraka M.    patch_full_name: 2020_31TCG_patch_14_14    patch_year: 2020    patch_name: patch_14_14    patch_country_code: ES    patch_tile: 31TCG    creation_date: 27 Apr 2021    references: Documentation available at .    institution: National Observatory of Athens.    version: 21.03    _format: NETCDF4    _nco_version: netCDF Operators version 4.9.1 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)    _xarray_version: 0.17.0    dimensions(sizes):    variables(dimensions):    groups: B01, B02, B03, B04, B05, B06, B07, B08, B09, B10, B11, B12, B8A, labels, parcels"""
  1. Visualize a single timestamp.
importxarrayasxrband_data=xr.open_dataset(xr.backends.NetCDF4DataStore(patch['B02']))band_data.B02.isel(time=0).plot()

Single Month

  1. Visualize the labels:
labels=xr.open_dataset(xr.backends.NetCDF4DataStore(patch['labels']))labels.labels.plot()

Labels

  1. Visualize the parcels:
parcels=xr.open_dataset(xr.backends.NetCDF4DataStore(patch['parcels']))parcels.parcels.plot()

Parcels

  1. Plot the median of observations for each month:
importpandasaspd# Or maybe aggregate based on a given frequency# Refer to# https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesgroup_freq='1MS'# Grab year from netcdf4's global attributeyear=patch.patch_year# output intervalsdate_range=pd.date_range(start=f'{year}-01-01',end=f'{int(year)+1}-01-01',freq=group_freq)# Aggregate based on given frequencyband_data=band_data.groupby_bins('time',bins=date_range,right=True,include_lowest=False,labels=date_range[:-1]).median(dim='time')

If you plot right now, you might notice that some months are empty:Single Month

(Optional) Fill in empty months:

importmatplotlib.pyplotaspltband_data=band_data.interpolate_na(dim='time_bins',method='linear',fill_value='extrapolate')fig,axes=plt.subplots(nrows=3,ncols=4,figsize=(18,12))fori,seasoninenumerate(band_data.B02):ax=axes.flat[i]cax=band_data.B02.isel(time_bins=i).plot(ax=ax)fori,axinenumerate(axes.flat):ax.axes.get_xaxis().set_ticklabels([])ax.axes.get_yaxis().set_ticklabels([])ax.axes.axis('tight')ax.set_xlabel('')ax.set_ylabel('')ax.set_title(f'Month:{i+1}')plt.tight_layout()plt.show()

Per Month

PatchesDataset usage example

Please refer to the providednotebook for a detailed usage example of the providedPatchesDataset.

  1. Read the COCO file to be used.
frompathlibimportPathfrompycocotools.cocoimportCOCOroot_path_coco=Path('coco_files/')coco_train=COCO(root_path_coco/'coco_example.json')
  1. Initialize the PatchesDataset.
fromtorch.utils.dataimportDataLoaderfrompatches_datasetimportPatchesDatasetfromutils.configimportLINEAR_ENCODERroot_path_netcdf=Path('dataset/netcdf')# Path to the netCDF filesdataset_train=PatchesDataset(root_path_netcdf=root_path_netcdf,coco=coco_train,group_freq='1MS',prefix='test_patchesdataset',bands=['B02','B03','B04'],linear_encoder=LINEAR_ENCODER,saved_medians=False,window_len=6,requires_norm=False,return_masks=False,clouds=False,cirrus=False,shadow=False,snow=False,output_size=(183,183)                              )
  1. Initialize the Dataloader.
dataloader_train=DataLoader(dataset_train,batch_size=1,shuffle=True,num_workers=4,pin_memory=True                             )
  1. Get a batch.
batch=next(iter(dataloader_train))

Thebatch variable is a dictionary containing the keys:medians,labels,idx.batch['medians'] contains a pytorch tensor of size[1, 6, 3, 183, 183] where:

  • batch size: 1
  • timestamps: 6
  • bands: 3
  • height: 183
  • width: 183

Batch Medians

batch['labels'] contains the corresponding labels of the medians, which is a pytorch tensor of size[1, 183, 183] where:

  • batch size: 1
  • height: 183
  • width: 183

Batch Labels

batch['idx'] contains the index of the returned timeseries.

Webpage

Dataset Webpage:https://www.sen4agrinet.space.noa.gr/

Experiments

Please visitSen4AgriNet-Models for a complete experimentation pipeline using the Sen4AgriNet dataset.

Citation

To cite please use:

@ARTICLE{  9749916,  author={Sykas, Dimitrios and Sdraka, Maria and Zografakis, Dimitrios and Papoutsis, Ioannis},  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},  title={A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning},  year={2022},  doi={10.1109/JSTARS.2022.3164771}}

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