Datasets:

DarthReca
/

quakeset

	# 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.


	import json
	import os

	import datasets
	import h5py
	import numpy as np
	import pandas as pd

	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """
	@misc{cambrin2024quakeset,
	title={QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1},
	author={Daniele Rege Cambrin and Paolo Garza},
	year={2024},
	eprint={2403.18116},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	QuakeSet is a dataset of earthquake images from the Copernicus Sentinel-1 satellites.
	It contains images from before, after an earthquake, and a sample before the "before" sample.
	Ground truth contains magnitudes and locations of earthquakes provided by ISC.
	"""

	_HOMEPAGE = "https://huggingface.co/datasets/DarthReca/quakeset"

	_LICENSE = "OPENRAIL"

	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLS = ["earthquakes.h5"]


	class QuakeSet(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	VERSION = datasets.Version("1.0.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="default",
	version=VERSION,
	description="Default configuration",
	)
	]

	DEFAULT_CONFIG_NAME = "default" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	if self.config.name == "default":
	features = datasets.Features(
	{
	"sample_key": datasets.Value("string"), # sample_id
	"pre_post_image": datasets.Array3D(
	shape=(4, 512, 512), dtype="float32"
	),
	"affected": datasets.ClassLabel(num_classes=2),
	"magnitude": datasets.Value("float32"),
	"hypocenter": datasets.Sequence(
	datasets.Value("float32"), length=3
	),
	"epsg": datasets.Value("int32"),
	"x": datasets.Sequence(datasets.Value("float32"), length=512),
	"y": datasets.Sequence(datasets.Value("float32"), length=512),
	}
	)

	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, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# 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):
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	urls = _URLS
	files = dl_manager.download(urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": files,
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": files,
	"split": "validation",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": files,
	"split": "test",
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	sample_ids = []
	with h5py.File(filepath[0]) as f:
	for key, patches in f.items():
	attributes = dict(f[key].attrs)
	if attributes["split"] != split:
	continue
	sample_ids += [(f"{key}/{p}", 1, attributes) for p in patches.keys()]
	sample_ids += [
	(f"{key}/{p}", 0, attributes)
	for p, v in patches.items()
	if "before" in v
	]

	for sample_id, label, attributes in sample_ids:
	if "x" in sample_id or "y" in sample_id:
	continue

	pre_key = "pre" if label == 1 else "before"
	post_key = "post" if label == 1 else "pre"
	pre_sample = f[sample_id][pre_key][...]
	post_sample = f[sample_id][post_key][...]
	pre_sample = np.nan_to_num(pre_sample, nan=0).transpose(2, 0, 1)
	post_sample = np.nan_to_num(post_sample, nan=0).transpose(2, 0, 1)
	sample = np.concatenate(
	[pre_sample, post_sample], axis=0, dtype=np.float32
	)
	sample_key = f"{sample_id}/{post_key}"
	item = {
	"sample_key": sample_key,
	"pre_post_image": sample,
	"epsg": attributes["epsg"],
	}

	resource_id, patch_id = sample_id.split("/")
	x = f[resource_id]["x"][...]
	y = f[resource_id]["y"][...]
	x_start = int(patch_id.split("_")[1]) % (x.shape[0] // 512)
	y_start = int(patch_id.split("_")[1]) // (x.shape[0] // 512)
	x = x[x_start * 512 : (x_start + 1) * 512]
	y = y[y_start * 512 : (y_start + 1) * 512]
	item \|= {
	"affected": label,
	"magnitude": np.float32(attributes["magnitude"]),
	"hypocenter": attributes["hypocenter"],
	"x": x.flatten(),
	"y": y.flatten(),
	}

	yield sample_key, item