:new: Added dataset loader

california_burned_areas.py

@@ -0,0 +1,194 @@
+# 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 typing import List
+
+import datasets
+import h5py
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@article{cabuar,
+  title={Ca{B}u{A}r: California {B}urned {A}reas dataset for delineation},
+  author={Rege Cambrin, Daniele and Colomba, Luca and Garza, Paolo},
+  journal={IEEE Geoscience and Remote Sensing Magazine},
+  doi={10.1109/MGRS.2023.3292467},
+  year={2023} 
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+CaBuAr dataset contains images from Sentinel-2 satellites taken before and after a wildfire. 
+The ground truth masks are provided by the California Department of Forestry and Fire Protection and they are mapped on the images.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/DarthReca/california_burned_areas"
+
+_LICENSE = "OPENRAIL"
+
+_URLS = "raw/pacthes/512x512.hdf5"
+
+
+class CaBuArConfig(datasets.BuilderConfig):
+    """BuilderConfig for CaBuAr.
+
+    Parameters
+    ----------
+
+    load_prefire: bool
+        whether to load prefire data
+    train_folds: List[int]
+        list of folds to use for training
+    validation_folds: List[int]
+        list of folds to use for validation
+    test_folds: List[int]
+        list of folds to use for testing
+    **kwargs
+        keyword arguments forwarded to super.
+    """
+
+    def __init__(
+        self,
+        load_prefire: bool,
+        train_folds: List[int],
+        validation_folds: List[int],
+        test_folds: List[int],
+        **kwargs
+    ):
+        super(CaBuArConfig, self).__init__(**kwargs)
+        self.load_prefire = load_prefire
+        self.train_folds = train_folds
+        self.validation_folds = validation_folds
+        self.test_folds = test_folds
+
+
+class CaBuAr(datasets.GeneratorBasedBuilder):
+    """California Burned Areas dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        CaBuArConfig(
+            name="post-fire",
+            version=VERSION,
+            description="Post-fire only version of the dataset",
+            load_prefire=False,
+            train_folds=None,
+            validation_folds=None,
+            test_folds=None,
+        ),
+        CaBuArConfig(
+            name="pre-post-fire",
+            version=VERSION,
+            description="Pre-fire and post-fire version of the dataset",
+            load_prefire=True,
+            train_folds=None,
+            validation_folds=None,
+            test_folds=None,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "post-fire"
+
+    def _info(self):
+        if self.config.name == "pre-post-fire":
+            features = (
+                datasets.Features(
+                    {
+                        "post_fire": datasets.Array3D((512, 512, 12)),
+                        "pre_fire": datasets.Array3D((512, 512, 12)),
+                        "mask": datasets.Array3D((512, 512, 1)),
+                    }
+                ),
+            )
+        else:
+            features = (
+                datasets.Features(
+                    {
+                        "post_fire": datasets.Array3D((512, 512, 12)),
+                        "mask": datasets.Array3D((512, 512, 12)),
+                    }
+                ),
+            )
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        h5_file = dl_manager.download(_URLS)
+        # Raise ValueError if train_folds, validation_folds or test_folds are not set
+        if (
+            self.config.train_folds is None
+            or self.config.validation_folds is None
+            or self.config.test_folds is None
+        ):
+            raise ValueError("train_folds, validation_folds and test_folds must be set")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "folds": self.config.train_folds,
+                    "load_prefire": self.config.load_prefire,
+                    "filepath": h5_file,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "folds:": self.config.validation_folds,
+                    "load_prefire": self.config.load_prefire,
+                    "filepath": h5_file,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "folds": self.config.test_folds,
+                    "load_prefire": self.config.load_prefire,
+                    "filepath": h5_file,
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, folds: List[int], load_prefire: bool, filepath):
+        with h5py.File(filepath, "r") as f:
+            for uuid, values in f.items():
+                if values.attrs["fold"] not in folds:
+                    continue
+                if load_prefire and "pre_fire" not in values:
+                    continue
+                sample = {
+                    "post_fire": values["post_fire"][...],
+                    "mask": values["mask"][...],
+                }
+                if load_prefire:
+                    sample["pre_fire"] = values["pre_fire"][...]
+                yield uuid, sample