# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from itertools import product import numpy as np import xarray as xr import netCDF4 import datasets from pathlib import Path _CITATION = """\ @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} } """ _DESCRIPTION = """\ Sen4AgriNet is a Sentinel-2 based time series multi country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learning. It is annotated from farmer declarations collected via the Land Parcel Identification System (LPIS) for harmonizing country wide labels. These declarations have only recently been made available as open data, allowing for the first time the labelling of satellite imagery from ground truth data. We proceed to propose and standardise a new crop type taxonomy across Europe that address Common Agriculture Policy (CAP) needs, based on the Food and Agriculture Organization (FAO) Indicative Crop Classification scheme. Sen4AgriNet is the only multi-country, multi-year dataset that includes all spectral information. It is constructed to cover the period 2016-2020 for Catalonia and France, while it can be extended to include additional countries. """ _HOMEPAGE = "https://www.sen4agrinet.space.noa.gr/" _LICENSE = "MIT License" _URL = 'https://huggingface.co/datasets/paren8esis/S4A/resolve/main/data' # The tiles of Catalonia CAT_TILES = ['31TBF', '31TCF', '31TCG', '31TDF', '31TDG'] # The tiles of France FR_TILES = ['31TCJ', '31TDK', '31TCL', '31TDM', '31UCP', '31UDR'] # The maximum indices for each patch PATCH_IDX = { '2019': { '31TBF': [29, 29], '31TCF': [29, 27], '31TCG': [29, 29], '31TDF': [15, 9], '31TDG': [29, 29], '31TCJ': [29, 29], '31TDK': [29, 29], '31TCL': [29, 29], '31TDM': [29, 29], '31UCP': [29, 29], '31UDR': [29, 29] }, '2020': { '31TBF': [29, 29], '31TCF': [29, 27], '31TCG': [29, 29], '31TDF': [15, 9], '31TDG': [29, 29] } } class S4A(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("0.0.1") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="complete", version=VERSION, description="All Sen4AgriNet data."), datasets.BuilderConfig(name="cat_2019", version=VERSION, description="Sen4AgriNet data for Catalonia 2019."), datasets.BuilderConfig(name="cat_2020", version=VERSION, description="Sen4AgriNet data for Catalonia 2020."), datasets.BuilderConfig(name="fr_2019", version=VERSION, description="Sen4AgriNet data for France 2019."), ] DEFAULT_CONFIG_NAME = "complete" def _info(self): features = datasets.Features( { "patch_full_name": datasets.Value("string"), "patch_year": datasets.Value("string"), "patch_name": datasets.Value("string"), "patch_country_code": datasets.Value("string"), "patch_tile": datasets.Value("string"), "B01": datasets.Array3D(shape=(None, 61, 61), dtype="uint16"), "B02": datasets.Array3D(shape=(None, 366, 366), dtype="uint16"), "B03": datasets.Array3D(shape=(None, 366, 366), dtype="uint16"), "B04": datasets.Array3D(shape=(None, 366, 366), dtype="uint16"), "B05": datasets.Array3D(shape=(None, 183, 183), dtype="uint16"), "B06": datasets.Array3D(shape=(None, 183, 183), dtype="uint16"), "B07": datasets.Array3D(shape=(None, 183, 183), dtype="uint16"), "B08": datasets.Array3D(shape=(None, 366, 366), dtype="uint16"), "B09": datasets.Array3D(shape=(None, 61, 61), dtype="uint16"), "B10": datasets.Array3D(shape=(None, 61, 61), dtype="uint16"), "B11": datasets.Array3D(shape=(None, 183, 183), dtype="uint16"), "B12": datasets.Array3D(shape=(None, 183, 183), dtype="uint16"), "B8A": datasets.Array3D(shape=(None, 183, 183), dtype="uint16"), "labels": datasets.Array2D(shape=(366, 366), dtype="uint32"), "parcels": datasets.Array2D(shape=(366, 366), dtype="uint32"), "timestamp": datasets.Sequence(datasets.Value("timestamp[ns]")) } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): root_paths = [] if self.config.name == "complete": for year, tile in list(product(['2019'], FR_TILES)) + list(product(['2019', '2020'], CAT_TILES)): x, y = PATCH_IDX[year][tile] for x_i in range(x + 1): for y_i in range(y + 1): try: downloaded_paths = dl_manager.download(_URL + f'/{year}' + f'/{tile}' + f'/{year}_{tile}_patch_{str(x_i).zfill(2)}_{str(y_i).zfill(2)}.nc') root_paths.append(downloaded_paths) except FileNotFoundError as e: continue elif self.config.name == 'cat_2019': year = '2019' for tile in CAT_TILES: x, y = PATCH_IDX[year][tile] for x_i in range(x + 1): for y_i in range(y + 1): try: downloaded_paths = dl_manager.download(_URL + f'/{year}' + f'/{tile}' + f'/{year}_{tile}_patch_{str(x_i).zfill(2)}_{str(y_i).zfill(2)}.nc') root_paths.append(downloaded_paths) except FileNotFoundError as e: continue elif self.config.name == 'cat_2020': year = '2020' for tile in CAT_TILES: x, y = PATCH_IDX[year][tile] for x_i in range(x + 1): for y_i in range(y + 1): try: downloaded_paths = dl_manager.download(_URL + f'/{year}' + f'/{tile}' + f'/{year}_{tile}_patch_{str(x_i).zfill(2)}_{str(y_i).zfill(2)}.nc') root_paths.append(downloaded_paths) except FileNotFoundError as e: continue elif self.config.name == 'fr_2019': year = '2019' for tile in FR_TILES: x, y = PATCH_IDX[year][tile] for x_i in range(x + 1): for y_i in range(y + 1): try: downloaded_paths = dl_manager.download(_URL + f'/{year}' + f'/{tile}' + f'/{year}_{tile}_patch_{str(x_i).zfill(2)}_{str(y_i).zfill(2)}.nc') root_paths.append(downloaded_paths) except FileNotFoundError as e: continue return [ datasets.SplitGenerator( name='self.config.name', # These kwargs will be passed to _generate_examples gen_kwargs={ "root_paths": root_paths, }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, root_paths): for file in root_paths: netcdf = netCDF4.Dataset(file) res = { "patch_full_name": netcdf.patch_full_name, "patch_year": netcdf.patch_year, "patch_name": netcdf.patch_name, "patch_country_code": netcdf.patch_country_code, "patch_tile": netcdf.patch_tile } time_recorded = False for variable in ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09', 'B10', 'B11', 'B12', 'B8A', 'labels', 'parcels']: v = xr.open_dataset(xr.backends.NetCDF4DataStore(netcdf[variable])) if not time_recorded: res['timestamp'] = (v.time.values.astype(np.int64) // 10 ** 9).tolist() time_recorded = True res[variable] = getattr(v, variable).values key = res['patch_full_name'] yield key, res