california_burned_areas.py

# 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/patched/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"
    BUILDER_CONFIG_CLASS = CaBuArConfig

    def _info(self):
        if self.config.name == "pre-post-fire":
            features = datasets.Features(
                {
                    "post_fire": datasets.Array3D((512, 512, 12), dtype="uint16"),
                    "pre_fire": datasets.Array3D((512, 512, 12), dtype="uint16"),
                    "mask": datasets.Array3D((512, 512, 1), dtype="uint16"),
                }
            )
        else:
            features = datasets.Features(
                {
                    "post_fire": datasets.Array3D((512, 512, 12), dtype="uint16"),
                    "mask": datasets.Array3D((512, 512, 12), dtype="uint16"),
                }
            )

        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