Source code for torchgeo.datasets.quakeset
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""QuakeSet dataset."""
import os
from collections.abc import Callable
from typing import Any, cast
import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib.figure import Figure
from torch import Tensor
from .errors import DatasetNotFoundError
from .geo import NonGeoDataset
from .utils import download_url, percentile_normalization
[docs]class QuakeSet(NonGeoDataset):
"""QuakeSet dataset.
`QuakeSet <https://huggingface.co/datasets/DarthReca/quakeset>`__
is a dataset for Earthquake Change Detection and Magnitude Estimation and is used
for the Seismic Monitoring and Analysis (SMAC) ECML-PKDD 2024 Discovery Challenge.
Dataset features:
* Sentinel-1 SAR imagery
* before/pre/post imagery of areas affected by earthquakes
* 2 SAR bands (VV/VH)
* 3,327 pairs of pre and post images with 5 m per pixel resolution (512x512 px)
* 2 classification labels (unaffected / affected by earthquake)
* pre/post image pairs represent earthquake affected areas
* before/pre image pairs represent hard negative unaffected areas
* earthquake magnitudes for each sample
Dataset format:
* single hdf5 dataset containing images, magnitudes, hypercenters, and splits
Dataset classes:
0. unaffected area
1. earthquake affected area
If you use this dataset in your research, please cite the following paper:
* https://arxiv.org/abs/2403.18116
.. note::
This dataset requires the following additional library to be installed:
* `h5py <https://pypi.org/project/h5py/>`_ to load the dataset
.. versionadded:: 0.6
"""
filename = 'earthquakes.h5'
url = 'https://hf.co/datasets/DarthReca/quakeset/resolve/bead1d25fb9979dbf703f9ede3e8b349f73b29f7/earthquakes.h5'
md5 = '76fc7c76b7ca56f4844d852e175e1560'
splits = {'train': 'train', 'val': 'validation', 'test': 'test'}
classes = ['unaffected_area', 'earthquake_affected_area']
[docs] def __init__(
self,
root: str = 'data',
split: str = 'train',
transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new QuakeSet dataset instance.
Args:
root: root directory where dataset can be found
split: one of "train", "val", or "test"
transforms: a function/transform that takes input sample and its target as
entry and returns a transformed version
download: if True, download dataset and store it in the root directory
checksum: if True, check the MD5 of the downloaded files (may be slow)
Raises:
AssertionError: If ``split`` argument is invalid.
DatasetNotFoundError: If dataset is not found and *download* is False.
ImportError: if h5py is not installed
"""
assert split in self.splits
self.root = root
self.split = split
self.transforms = transforms
self.download = download
self.checksum = checksum
self.filepath = os.path.join(root, self.filename)
self._verify()
try:
import h5py # noqa: F401
except ImportError:
raise ImportError(
'h5py is not installed and is required to use this dataset'
)
self.data = self._load_data()
[docs] def __getitem__(self, index: int) -> dict[str, Tensor]:
"""Return an index within the dataset.
Args:
index: index to return
Returns:
sample containing image and mask
"""
image = self._load_image(index)
label = torch.tensor(self.data[index]['label'])
magnitude = torch.tensor(self.data[index]['magnitude'])
sample = {'image': image, 'label': label, 'magnitude': magnitude}
if self.transforms is not None:
sample = self.transforms(sample)
return sample
[docs] def __len__(self) -> int:
"""Return the number of data points in the dataset.
Returns:
length of the dataset
"""
return len(self.data)
def _load_data(self) -> list[dict[str, Any]]:
"""Return the metadata for a given split.
Returns:
the sample keys, patches, images, labels, and magnitudes
"""
import h5py
data = []
with h5py.File(self.filepath) as f:
for k in sorted(f.keys()):
if f[k].attrs['split'] != self.splits[self.split]:
continue
for patch in sorted(f[k].keys()):
if patch not in ['x', 'y']:
# positive sample
magnitude = float(f[k].attrs['magnitude'])
data.append(
dict(
key=k,
patch=patch,
images=('pre', 'post'),
label=1,
magnitude=magnitude,
)
)
# hard negative sample
if 'before' in f[k][patch].keys():
data.append(
dict(
key=k,
patch=patch,
images=('before', 'pre'),
label=0,
magnitude=0.0,
)
)
return data
def _load_image(self, index: int) -> Tensor:
"""Load a single image.
Args:
index: index to return
Returns:
the image
"""
import h5py
key = self.data[index]['key']
patch = self.data[index]['patch']
images = self.data[index]['images']
with h5py.File(self.filepath) as f:
pre_array = f[key][patch][images[0]][:]
pre_array = np.nan_to_num(pre_array, nan=0)
post_array = f[key][patch][images[1]][:]
post_array = np.nan_to_num(post_array, nan=0)
array = np.concatenate([pre_array, post_array], axis=-1)
array = array.astype(np.float32)
tensor = torch.from_numpy(array)
# Convert from HxWxC to CxHxW
tensor = tensor.permute((2, 0, 1))
return tensor
def _verify(self) -> None:
"""Verify the integrity of the dataset."""
# Check if the files already exist
if os.path.exists(self.filepath):
return
# Check if the user requested to download the dataset
if not self.download:
raise DatasetNotFoundError(self)
# Download the dataset
self._download()
def _download(self) -> None:
"""Download the dataset."""
if not os.path.exists(self.filepath):
download_url(
self.url,
self.root,
filename=self.filename,
md5=self.md5 if self.checksum else None,
)
[docs] def plot(
self,
sample: dict[str, Tensor],
show_titles: bool = True,
suptitle: str | None = None,
) -> Figure:
"""Plot a sample from the dataset.
Args:
sample: a sample returned by :meth:`__getitem__`
show_titles: flag indicating whether to show titles above each panel
suptitle: optional suptitle to use for figure
Returns:
a matplotlib Figure with the rendered sample
"""
image = sample['image'].permute((1, 2, 0)).numpy()
label = cast(int, sample['label'].item())
label_class = self.classes[label]
# Create false color image for image1
vv = percentile_normalization(image[..., 0]) + 1e-16
vh = percentile_normalization(image[..., 1]) + 1e-16
fci1 = np.stack([vv, vh, vv / vh], axis=-1).clip(0, 1)
# Create false color image for image2
vv = percentile_normalization(image[..., 2]) + 1e-16
vh = percentile_normalization(image[..., 3]) + 1e-16
fci2 = np.stack([vv, vh, vv / vh], axis=-1).clip(0, 1)
showing_predictions = 'prediction' in sample
if showing_predictions:
prediction = cast(int, sample['prediction'].item())
prediction_class = self.classes[prediction]
ncols = 2
fig, axs = plt.subplots(
nrows=1, ncols=ncols, figsize=(ncols * 5, 10), sharex=True
)
axs[0].imshow(fci1)
axs[0].axis('off')
axs[0].set_title('Image Pre')
axs[1].imshow(fci2)
axs[1].axis('off')
axs[1].set_title('Image Post')
if show_titles:
title = f'Label: {label_class}'
if 'magnitude' in sample:
magnitude = cast(float, sample['magnitude'].item())
title += f' | Magnitude: {magnitude:.2f}'
if showing_predictions:
title += f'\nPrediction: {prediction_class}'
fig.supxlabel(title, y=0.22)
if suptitle is not None:
fig.suptitle(suptitle, y=0.8)
fig.tight_layout()
return fig
