initial commit

S4A.py

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+# 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_test/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_TEST(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="complete", version=VERSION, description="All Sen4AgriNet data."),
+        datasets.BuilderConfig(name="tiny", version=VERSION, description="Just three samples for testing."),
+        datasets.BuilderConfig(name="cat_2019", version=VERSION, description="Sen4AgriNet data for Catalonia 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):
+                        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)
+
+            return [
+                datasets.SplitGenerator(
+                    name='complete',
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "root_paths": root_paths,
+                    },
+                ),
+            ]
+        elif self.config.name == 'tiny':
+            selected_files = ['2019_31TCF_patch_00_11.nc', '2019_31UCP_patch_01_19.nc', '2020_31TDG_patch_11_17.nc']
+
+            for file in selected_files:
+                year, tile = file.split('_')[:2]
+
+                downloaded_paths = dl_manager.download(_URL + f'/{year}' + f'/{tile}' + f'/{file}')
+                root_paths.append(downloaded_paths)
+
+            return [
+                datasets.SplitGenerator(
+                    name='tiny',
+                    # 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
+            

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