Datasets:

DarthReca
/

california_burned_areas

	# 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, **kwargs):
	super(CaBuArConfig, self).__init__(**kwargs)
	self.load_prefire = load_prefire


	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,
	),
	CaBuArConfig(
	name="pre-post-fire",
	version=VERSION,
	description="Pre-fire and post-fire version of the dataset",
	load_prefire=True,
	),
	]

	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, 1), 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)

	return [
	datasets.SplitGenerator(
	name=fold,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"fold": fold,
	"load_prefire": self.config.load_prefire,
	"filepath": h5_file,
	},
	)
	for fold in range(0, 5)
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, fold: int, load_prefire: bool, filepath):
	with h5py.File(filepath, "r") as f:
	for uuid, values in f.items():
	if values.attrs["fold"] != fold:
	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