Source code for torchgeo.datasets.ssl4eo
# Copyright (c) TorchGeo Contributors. All rights reserved.
# Licensed under the MIT License.
"""Self-Supervised Learning for Earth Observation."""
import glob
import os
import random
import re
from collections.abc import Callable
from typing import ClassVar, Literal, TypedDict
import matplotlib.pyplot as plt
import numpy as np
import rasterio
import torch
from matplotlib.figure import Figure
from torch import Tensor
from .errors import DatasetNotFoundError
from .geo import NonGeoDataset
from .landsat import Landsat, Landsat5TM, Landsat7, Landsat8
from .sentinel import Sentinel1, Sentinel2
from .utils import Path, disambiguate_timestamp, download_url, extract_archive
[docs]class SSL4EO(NonGeoDataset):
"""Base class for all SSL4EO datasets.
Self-Supervised Learning for Earth Observation (SSL4EO) is a collection of
large-scale multimodal multitemporal datasets for unsupervised/self-supervised
pre-training in Earth observation.
.. versionadded:: 0.5
"""
[docs]class SSL4EOL(SSL4EO):
"""SSL4EO-L dataset.
Landsat version of SSL4EO.
The dataset consists of a parallel corpus (same locations and dates for SR/TOA)
for the following sensors:
.. list-table::
:widths: 10 10 10 10 10 10
:header-rows: 1
* - Split
- Satellites
- Sensors
- Level
- # Bands
- Link
* - tm_toa
- Landsat 4--5
- TM
- TOA
- 7
- `GEE <https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C02_T1_TOA>`__
* - etm_sr
- Landsat 7
- ETM+
- SR
- 6
- `GEE <https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C02_T1_L2>`__
* - etm_toa
- Landsat 7
- ETM+
- TOA
- 9
- `GEE <https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_TOA>`__
* - oli_tirs_toa
- Landsat 8--9
- OLI+TIRS
- TOA
- 11
- `GEE <https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_TOA>`__
* - oli_sr
- Landsat 8--9
- OLI
- SR
- 7
- `GEE <https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2>`__
Each patch has the following properties:
* 264 x 264 pixels
* Resampled to 30 m resolution (7920 x 7920 m)
* 4 seasonal timestamps
* Single multispectral GeoTIFF file
.. note::
Each split is 300--400 GB and requires 3x that to concatenate and extract
tarballs. Tarballs can be safely deleted after extraction to save space.
The dataset takes about 1.5 hrs to download and checksum and another 3 hrs
to extract.
If you use this dataset in your research, please cite the following paper:
* https://proceedings.neurips.cc/paper_files/paper/2023/hash/bbf7ee04e2aefec136ecf60e346c2e61-Abstract-Datasets_and_Benchmarks.html
.. versionadded:: 0.5
"""
class _Metadata(TypedDict):
all_bands: list[str]
rgb_bands: list[int]
metadata: ClassVar[dict[str, _Metadata]] = {
'tm_toa': {
'all_bands': ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7'],
'rgb_bands': [2, 1, 0],
},
'etm_toa': {
'all_bands': ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B6', 'B7', 'B8'],
'rgb_bands': [2, 1, 0],
},
'etm_sr': {
'all_bands': ['B1', 'B2', 'B3', 'B4', 'B5', 'B7'],
'rgb_bands': [2, 1, 0],
},
'oli_tirs_toa': {
'all_bands': [
'B1',
'B2',
'B3',
'B4',
'B5',
'B6',
'B7',
'B8',
'B9',
'B10',
'B11',
],
'rgb_bands': [3, 2, 1],
},
'oli_sr': {
'all_bands': ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7'],
'rgb_bands': [3, 2, 1],
},
}
url = 'https://hf.co/datasets/torchgeo/ssl4eo_l/resolve/e2467887e6a6bcd7547d9d5999f8d9bc3323dc31/{0}/ssl4eo_l_{0}.tar.gz{1}'
checksums: ClassVar[dict[str, dict[str, str]]] = {
'tm_toa': {
'aa': '553795b8d73aa253445b1e67c5b81f11',
'ab': 'e9e0739b5171b37d16086cb89ab370e8',
'ac': '6cb27189f6abe500c67343bfcab2432c',
'ad': '15a885d4f544d0c1849523f689e27402',
'ae': '35523336bf9f8132f38ff86413dcd6dc',
'af': 'fa1108436034e6222d153586861f663b',
'ag': 'd5c91301c115c00acaf01ceb3b78c0fe',
},
'etm_toa': {
'aa': '587c3efc7d0a0c493dfb36139d91ccdf',
'ab': 'ec34f33face893d2d8fd152496e1df05',
'ac': '947acc2c6bc3c1d1415ac92bab695380',
'ad': 'e31273dec921e187f5c0dc73af5b6102',
'ae': '43390a47d138593095e9a6775ae7dc75',
'af': '082881464ca6dcbaa585f72de1ac14fd',
'ag': 'de2511aaebd640bd5e5404c40d7494cb',
'ah': '124c5fbcda6871f27524ae59480dabc5',
'ai': '12b5f94824b7f102df30a63b1139fc57',
},
'etm_sr': {
'aa': 'baa36a9b8e42e234bb44ab4046f8f2ac',
'ab': '9fb0f948c76154caabe086d2d0008fdf',
'ac': '99a55367178373805d357a096d68e418',
'ad': '59d53a643b9e28911246d4609744ef25',
'ae': '7abfcfc57528cb9c619c66ee307a2cc9',
'af': 'bb23cf26cc9fe156e7a68589ec69f43e',
'ag': '97347e5a81d24c93cf33d99bb46a5b91',
},
'oli_tirs_toa': {
'aa': '4711369b861c856ebfadbc861e928d3a',
'ab': '660a96cda1caf54df837c4b3c6c703f6',
'ac': 'c9b6a1117916ba318ac3e310447c60dc',
'ad': 'b8502e9e92d4a7765a287d21d7c9146c',
'ae': '5c11c14cfe45f78de4f6d6faf03f3146',
'af': '5b0ed3901be1000137ddd3a6d58d5109',
'ag': 'a3b6734f8fe6763dcf311c9464a05d5b',
'ah': '5e55f92e3238a8ab3e471be041f8111b',
'ai': 'e20617f73d0232a0c0472ce336d4c92f',
},
'oli_sr': {
'aa': 'ca338511c9da4dcbfddda28b38ca9e0a',
'ab': '7f4100aa9791156958dccf1bb2a88ae0',
'ac': '6b0f18be2b63ba9da194cc7886dbbc01',
'ad': '57efbcc894d8da8c4975c29437d8b775',
'ae': '2594a0a856897f3f5a902c830186872d',
'af': 'a03839311a2b3dc17dfb9fb9bc4f9751',
'ag': '6a329d8fd9fdd591e400ab20f9d11dea',
},
}
[docs] def __init__(
self,
root: Path = 'data',
split: Literal[
'tm_toa', 'etm_toa', 'etm_sr', 'oli_tirs_toa', 'oli_sr'
] = 'oli_sr',
seasons: Literal[1, 2, 3, 4] = 1,
transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new SSL4EOL instance.
Args:
root: root directory where dataset can be found
split: one of ['tm_toa', 'etm_toa', 'etm_sr', 'oli_tirs_toa', 'oli_sr']
seasons: number of seasonal patches to sample per location, 1--4
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 after downloading files (may be slow)
Raises:
DatasetNotFoundError: If dataset is not found and *download* is False.
"""
self.root = root
self.subdir = os.path.join(root, f'ssl4eo_l_{split}')
self.split = split
self.seasons = seasons
self.transforms = transforms
self.download = download
self.checksum = checksum
self._verify()
if split.startswith('tm'):
base: type[Landsat] = Landsat5TM
elif split.startswith('etm'):
base = Landsat7
else:
base = Landsat8
self.wavelengths = []
for band in self.metadata[split]['all_bands']:
self.wavelengths.append(base.wavelengths[band])
self.scenes = sorted(os.listdir(self.subdir))
[docs] def __getitem__(self, index: int) -> dict[str, Tensor]:
"""Return an index within the dataset.
Args:
index: index to return
Returns:
image sample
"""
root = os.path.join(self.subdir, self.scenes[index])
subdirs = os.listdir(root)
subdirs = random.sample(subdirs, self.seasons)
images = []
xs = []
ys = []
ts = []
wavelengths = []
for subdir in subdirs:
mint, maxt = disambiguate_timestamp(subdir[-8:], Landsat.date_format)
directory = os.path.join(root, subdir)
filename = os.path.join(directory, 'all_bands.tif')
with rasterio.open(filename) as f:
minx, maxx = f.bounds.left, f.bounds.right
miny, maxy = f.bounds.bottom, f.bounds.top
image = f.read()
images.append(torch.from_numpy(image.astype(np.float32)))
xs.append((minx + maxx) / 2)
ys.append((miny + maxy) / 2)
ts.append((mint.timestamp() + maxt.timestamp()) / 2)
wavelengths.extend(self.wavelengths)
sample = {
'image': torch.cat(images),
'x': torch.tensor(xs),
'y': torch.tensor(ys),
't': torch.tensor(ts),
'wavelength': torch.tensor(wavelengths),
'res': torch.tensor(30),
}
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.scenes)
def _verify(self) -> None:
"""Verify the integrity of the dataset."""
# Check if the extracted files already exist
path = os.path.join(self.subdir, '00000*', '*', 'all_bands.tif')
if glob.glob(path):
return
# Check if the tar.gz files have already been downloaded
exists = []
for suffix in self.checksums[self.split]:
path = self.subdir + f'.tar.gz{suffix}'
exists.append(os.path.exists(path))
if all(exists):
self._extract()
return
# Check if the user requested to download the dataset
if not self.download:
raise DatasetNotFoundError(self)
# Download the dataset
self._download()
self._extract()
def _download(self) -> None:
"""Download the dataset."""
for suffix, md5 in self.checksums[self.split].items():
download_url(
self.url.format(self.split, suffix),
self.root,
md5=md5 if self.checksum else None,
)
def _extract(self) -> None:
"""Extract the dataset."""
# Concatenate all tarballs together
chunk_size = 2**15 # same as torchvision
path = self.subdir + '.tar.gz'
with open(path, 'wb') as f:
for suffix in self.checksums[self.split]:
with open(path + suffix, 'rb') as g:
while chunk := g.read(chunk_size):
f.write(chunk)
# Extract the concatenated tarball
extract_archive(path)
[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 string to use as a suptitle
Returns:
a matplotlib Figure with the rendered sample
"""
fig, axes = plt.subplots(
ncols=self.seasons, squeeze=False, figsize=(4 * self.seasons, 4)
)
num_bands = len(self.metadata[self.split]['all_bands'])
rgb_bands = self.metadata[self.split]['rgb_bands']
for i in range(self.seasons):
image = sample['image'][i * num_bands : (i + 1) * num_bands].byte()
image = image[rgb_bands].permute(1, 2, 0)
axes[0, i].imshow(image)
axes[0, i].axis('off')
if show_titles:
axes[0, i].set_title(f'Split {self.split}, Season {i + 1}')
if suptitle is not None:
plt.suptitle(suptitle)
return fig
[docs]class SSL4EOS12(SSL4EO):
"""SSL4EO-S12 dataset.
`Sentinel-1/2 <https://github.com/zhu-xlab/SSL4EO-S12>`_ version of SSL4EO.
The dataset consists of a parallel corpus (same locations and dates)
for the following satellites:
.. list-table::
:widths: 10 10 10 10 10
:header-rows: 1
* - Split
- Satellite
- Level
- # Bands
- Link
* - s1
- Sentinel-1
- GRD
- 2
- `GEE <https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD>`__
* - s2c
- Sentinel-2
- TOA
- 12
- `GEE <https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED>`__
* - s2a
- Sentinel-2
- SR
- 13
- `GEE <https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED>`__
Each patch has the following properties:
* 264 x 264 pixels
* Resampled to 10 m resolution (2640 x 2640 m)
* 4 seasonal timestamps
If you use this dataset in your research, please cite the following paper:
* https://arxiv.org/abs/2211.07044
.. note::
The dataset is about 1.5 TB when compressed and 3.7 TB when uncompressed.
.. versionadded:: 0.5
"""
size = 264
class _Metadata(TypedDict):
bands: list[str]
filename_regex: str
metadata: ClassVar[dict[str, _Metadata]] = {
's1': {
'bands': ['VV', 'VH'],
'filename_regex': r'^.{16}_(?P<date>\d{8}T\d{6})',
},
's2c': {
'bands': [
'B1',
'B2',
'B3',
'B4',
'B5',
'B6',
'B7',
'B8',
'B8A',
'B9',
'B10',
'B11',
'B12',
],
'filename_regex': r'^(?P<date>\d{8}T\d{6})',
},
's2a': {
'bands': [
'B1',
'B2',
'B3',
'B4',
'B5',
'B6',
'B7',
'B8',
'B8A',
'B9',
'B11',
'B12',
],
'filename_regex': r'^(?P<date>\d{8}T\d{6})',
},
}
url = 'https://hf.co/datasets/wangyi111/SSL4EO-S12/resolve/3f5ddad68ba2ea29d019b0cef6cf292ff8af0d62/{0}/{0}.tar.gz.part{1}'
filenames: ClassVar[dict[str, str]] = {
's1': 's1_grd',
's2c': 's2_l1c',
's2a': 's2_l2a',
}
checksums: ClassVar[dict[str, dict[str, str]]] = {
's1': {
'aa': '6df278053fc3e4c3fd7de2f77856e606',
'ab': '837755b4ba8d82faf254df9e5fec13a7',
'ac': '6400423305d6084e2006eede75cf288e',
'ad': '22a50d7362d9cbc9714e0740fe2122c7',
'ae': 'd6ac97ead00b4296a95376949c946b12',
'af': 'd8047814061431dc627b9ae345c80891',
'ag': '089ce0548cb7902ce873181cc61f5d70',
'ah': '745b48c2896ca764ef54f91e4e7c555e',
'ai': 'c36595cf9617b3b7ea722f63dcccbedc',
'aj': 'cf16f1d81e8bff2d663e4eba79ec6fa3',
},
's2c': {
'aa': 'a3ef419cc65c4d8ac19b9acc55726166',
'ab': '580451e8fdf93067ad79202b95dd1a5c',
'ac': 'a6f7318868f5ba1d94fb9363b50307e4',
'ad': '86f324215b04cdf4242d07aaf3cdfe57',
'ae': '5895a545460f34b1712c17732e0f5533',
'af': '078078bc58d8ecc214ddfd838f796700',
'ag': '3557dd4c24a5942020391a5baaf51abb',
'ah': 'd59f89271e414648663d3acb66121761',
'ai': '1a213539c989d16da4e5b4e09feaa98a',
'aj': '0b229af5633c7f63486b6d7771b737db',
'ak': 'babe8bed884d31b891151f5717a83b5c',
'al': '8d1f5ad28ee868ab0595c889446b8e5f',
},
's2a': {
'aa': 'ef847d906ab44cc9a94d086a89473833',
'ab': '4a6a8ed9e2a08887707d83bcb6eb57af',
'ac': '00b706a771df4c4df4cc70a20d790339',
'ad': '579024e84bd9ab0b86e1182792c8dcf9',
'ae': 'e259f3536355b665aea490c22c897e59',
'af': '2a15be319ad15f749bfd4ed85d14c172',
'ag': 'd8224cff1e727543473b0111e307110c',
'ah': '0015d8aa5ea9201e13b401fd61c36c6f',
'ai': 'dfce87c0a9550177fd4b82887902b6e3',
'aj': '688392701760b737ad74cb0e8c7fb731',
'ak': 'd8f3e4b110f22f0477973ed2c35586b6',
'al': '1cc3641cd52afedaa1c50d14d84a6664',
},
}
[docs] def __init__(
self,
root: Path = 'data',
split: Literal['s1', 's2c', 's2a'] = 's2c',
seasons: Literal[1, 2, 3, 4] = 1,
transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new SSL4EOS12 instance.
Args:
root: root directory where dataset can be found
split: one of "s1" (Sentinel-1 GRD dual-pol SAR),
"s2c" (Sentinel-2 Level-1C top-of-atmosphere reflectance), or
"s2a" (Sentinel-2 Level-2A surface reflectance)
seasons: number of seasonal patches to sample per location, 1--4
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:
DatasetNotFoundError: If dataset is not found and *download* is False.
.. versionadded:: 0.7
The *download* parameter.
"""
self.root = root
self.split = split
self.seasons = seasons
self.transforms = transforms
self.download = download
self.checksum = checksum
self._verify()
self.bands = self.metadata[self.split]['bands']
[docs] def __getitem__(self, index: int) -> dict[str, Tensor]:
"""Return an index within the dataset.
Args:
index: index to return
Returns:
image sample
"""
root = os.path.join(self.root, self.split, f'{index:07}')
subdirs = os.listdir(root)
subdirs = random.sample(subdirs, self.seasons)
filename_regex = self.metadata[self.split]['filename_regex']
images = []
xs = []
ys = []
ts = []
wavelengths: list[float] = []
for subdir in subdirs:
directory = os.path.join(root, subdir)
if match := re.match(filename_regex, subdir):
date_str = match.group('date')
match self.split:
case 's1':
date_format = Sentinel1.date_format
case 's2c' | 's2a':
date_format = Sentinel2.date_format
mint, maxt = disambiguate_timestamp(date_str, date_format)
for band in self.bands:
match self.split:
case 's1':
wavelengths.append(Sentinel1.wavelength)
case 's2c' | 's2a':
wavelengths.append(Sentinel2.wavelengths[band])
filename = os.path.join(directory, f'{band}.tif')
with rasterio.open(filename) as f:
minx, maxx = f.bounds.left, f.bounds.right
miny, maxy = f.bounds.bottom, f.bounds.top
image = f.read(out_shape=(1, self.size, self.size))
images.append(torch.from_numpy(image.astype(np.float32)))
xs.append((minx + maxx) / 2)
ys.append((miny + maxy) / 2)
ts.append((mint.timestamp() + maxt.timestamp()) / 2)
sample = {
'image': torch.cat(images),
'x': torch.tensor(xs),
'y': torch.tensor(ys),
't': torch.tensor(ts),
'wavelength': torch.tensor(wavelengths),
'res': torch.tensor(10),
}
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 251079
def _verify(self) -> None:
"""Verify the integrity of the dataset."""
# Check if the extracted files already exist
path = os.path.join(self.root, self.split, '00000*', '*', '*.tif')
if glob.glob(path):
return
# Check if the tar.gz files have already been downloaded
exists = []
for suffix in self.checksums[self.split]:
path = os.path.join(
self.root, self.filenames[self.split] + f'.tar.gz.part{suffix}'
)
exists.append(os.path.exists(path))
if all(exists):
self._extract()
return
# Check if the user requested to download the dataset
if not self.download:
raise DatasetNotFoundError(self)
# Download the dataset
self._download()
self._extract()
def _download(self) -> None:
"""Download the dataset."""
for suffix, md5 in self.checksums[self.split].items():
download_url(
self.url.format(self.filenames[self.split], suffix),
self.root,
md5=md5 if self.checksum else None,
)
def _extract(self) -> None:
"""Extract the dataset."""
# Concatenate all tarballs together
chunk_size = 2**15 # same as torchvision
path = os.path.join(self.root, self.filenames[self.split] + '.tar.gz')
with open(path, 'wb') as f:
for suffix in self.checksums[self.split]:
with open(f'{path}.part{suffix}', 'rb') as g:
while chunk := g.read(chunk_size):
f.write(chunk)
# Extract the concatenated tarball
extract_archive(path)
[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 string to use as a suptitle
Returns:
a matplotlib Figure with the rendered sample
"""
nrows = 2 if self.split == 's1' else 1
fig, axes = plt.subplots(
nrows=nrows,
ncols=self.seasons,
squeeze=False,
figsize=(4 * self.seasons, 4 * nrows),
)
for i in range(self.seasons):
image = sample['image'][i * len(self.bands) : (i + 1) * len(self.bands)]
if self.split == 's1':
axes[0, i].imshow(image[0])
axes[1, i].imshow(image[1])
else:
image = image[[3, 2, 1]].permute(1, 2, 0)
image = torch.clamp(image / 3000, min=0, max=1)
axes[0, i].imshow(image)
axes[0, i].axis('off')
if show_titles:
axes[0, i].set_title(f'Split {self.split}, Season {i + 1}')
if suptitle is not None:
plt.suptitle(suptitle)
return fig
