Source code for torchgeo.datasets.bigearthnet
# Copyright (c) TorchGeo Contributors. All rights reserved.
# Licensed under the MIT License.
"""BigEarthNet dataset."""
import glob
import json
import os
import textwrap
from collections.abc import Callable
from typing import ClassVar
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import rasterio
import torch
from matplotlib.colors import BoundaryNorm, ListedColormap
from matplotlib.figure import Figure
from matplotlib.patches import Rectangle
from rasterio.enums import Resampling
from torch import Tensor
from .errors import DatasetNotFoundError
from .geo import NonGeoDataset
from .utils import Path, download_url, extract_archive, sort_sentinel2_bands
[docs]class BigEarthNet(NonGeoDataset):
"""BigEarthNet dataset.
The `BigEarthNet <https://bigearth.net/>`__
dataset is a dataset for multilabel remote sensing image scene classification.
Dataset features:
* 590,326 patches from 125 Sentinel-1 and Sentinel-2 tiles
* Imagery from tiles in Europe between Jun 2017 - May 2018
* 12 spectral bands with 10-60 m per pixel resolution (base 120x120 px)
* 2 synthetic aperture radar bands (120x120 px)
* 43 or 19 scene classes from the 2018 CORINE Land Cover database (CLC 2018)
Dataset format:
* images are composed of multiple single channel geotiffs
* labels are multiclass, stored in a single json file per image
* mapping of Sentinel-1 to Sentinel-2 patches are within Sentinel-1 json files
* Sentinel-1 bands: (VV, VH)
* Sentinel-2 bands: (B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12)
* All bands: (VV, VH, B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12)
* Sentinel-2 bands are of different spatial resolutions and upsampled to 10m
Dataset classes (43):
0. Continuous urban fabric
1. Discontinuous urban fabric
2. Industrial or commercial units
3. Road and rail networks and associated land
4. Port areas
5. Airports
6. Mineral extraction sites
7. Dump sites
8. Construction sites
9. Green urban areas
10. Sport and leisure facilities
11. Non-irrigated arable land
12. Permanently irrigated land
13. Rice fields
14. Vineyards
15. Fruit trees and berry plantations
16. Olive groves
17. Pastures
18. Annual crops associated with permanent crops
19. Complex cultivation patterns
20. Land principally occupied by agriculture, with significant
areas of natural vegetation
21. Agro-forestry areas
22. Broad-leaved forest
23. Coniferous forest
24. Mixed forest
25. Natural grassland
26. Moors and heathland
27. Sclerophyllous vegetation
28. Transitional woodland/shrub
29. Beaches, dunes, sands
30. Bare rock
31. Sparsely vegetated areas
32. Burnt areas
33. Inland marshes
34. Peatbogs
35. Salt marshes
36. Salines
37. Intertidal flats
38. Water courses
39. Water bodies
40. Coastal lagoons
41. Estuaries
42. Sea and ocean
Dataset classes (19):
0. Urban fabric
1. Industrial or commercial units
2. Arable land
3. Permanent crops
4. Pastures
5. Complex cultivation patterns
6. Land principally occupied by agriculture, with significant
areas of natural vegetation
7. Agro-forestry areas
8. Broad-leaved forest
9. Coniferous forest
10. Mixed forest
11. Natural grassland and sparsely vegetated areas
12. Moors, heathland and sclerophyllous vegetation
13. Transitional woodland, shrub
14. Beaches, dunes, sands
15. Inland wetlands
16. Coastal wetlands
17. Inland waters
18. Marine waters
The source for the above dataset classes, their respective ordering, and
43-to-19-class mappings can be found here:
* https://git.tu-berlin.de/rsim/BigEarthNet-S2_19-classes_models/-/blob/master/label_indices.json
If you use this dataset in your research, please cite the following paper:
* https://doi.org/10.1109/IGARSS.2019.8900532
"""
class_sets: ClassVar[dict[int, list[str]]] = {
19: [
'Urban fabric',
'Industrial or commercial units',
'Arable land',
'Permanent crops',
'Pastures',
'Complex cultivation patterns',
'Land principally occupied by agriculture, with significant areas of'
' natural vegetation',
'Agro-forestry areas',
'Broad-leaved forest',
'Coniferous forest',
'Mixed forest',
'Natural grassland and sparsely vegetated areas',
'Moors, heathland and sclerophyllous vegetation',
'Transitional woodland, shrub',
'Beaches, dunes, sands',
'Inland wetlands',
'Coastal wetlands',
'Inland waters',
'Marine waters',
],
43: [
'Continuous urban fabric',
'Discontinuous urban fabric',
'Industrial or commercial units',
'Road and rail networks and associated land',
'Port areas',
'Airports',
'Mineral extraction sites',
'Dump sites',
'Construction sites',
'Green urban areas',
'Sport and leisure facilities',
'Non-irrigated arable land',
'Permanently irrigated land',
'Rice fields',
'Vineyards',
'Fruit trees and berry plantations',
'Olive groves',
'Pastures',
'Annual crops associated with permanent crops',
'Complex cultivation patterns',
'Land principally occupied by agriculture, with significant areas of'
' natural vegetation',
'Agro-forestry areas',
'Broad-leaved forest',
'Coniferous forest',
'Mixed forest',
'Natural grassland',
'Moors and heathland',
'Sclerophyllous vegetation',
'Transitional woodland/shrub',
'Beaches, dunes, sands',
'Bare rock',
'Sparsely vegetated areas',
'Burnt areas',
'Inland marshes',
'Peatbogs',
'Salt marshes',
'Salines',
'Intertidal flats',
'Water courses',
'Water bodies',
'Coastal lagoons',
'Estuaries',
'Sea and ocean',
],
}
label_converter: ClassVar[dict[int, int]] = {
0: 0,
1: 0,
2: 1,
11: 2,
12: 2,
13: 2,
14: 3,
15: 3,
16: 3,
18: 3,
17: 4,
19: 5,
20: 6,
21: 7,
22: 8,
23: 9,
24: 10,
25: 11,
31: 11,
26: 12,
27: 12,
28: 13,
29: 14,
33: 15,
34: 15,
35: 16,
36: 16,
38: 17,
39: 17,
40: 18,
41: 18,
42: 18,
}
splits_metadata: ClassVar[dict[str, dict[str, str]]] = {
'train': {
'url': 'https://git.tu-berlin.de/rsim/BigEarthNet-MM_19-classes_models/-/raw/9a5be07346ab0884b2d9517475c27ef9db9b5104/splits/train.csv?inline=false',
'filename': 'bigearthnet-train.csv',
'md5': '623e501b38ab7b12fe44f0083c00986d',
},
'val': {
'url': 'https://git.tu-berlin.de/rsim/BigEarthNet-MM_19-classes_models/-/raw/9a5be07346ab0884b2d9517475c27ef9db9b5104/splits/val.csv?inline=false',
'filename': 'bigearthnet-val.csv',
'md5': '22efe8ed9cbd71fa10742ff7df2b7978',
},
'test': {
'url': 'https://git.tu-berlin.de/rsim/BigEarthNet-MM_19-classes_models/-/raw/9a5be07346ab0884b2d9517475c27ef9db9b5104/splits/test.csv?inline=false',
'filename': 'bigearthnet-test.csv',
'md5': '697fb90677e30571b9ac7699b7e5b432',
},
}
metadata: ClassVar[dict[str, dict[str, str]]] = {
's1': {
'url': 'https://zenodo.org/records/12687186/files/BigEarthNet-S1-v1.0.tar.gz',
'md5': '94ced73440dea8c7b9645ee738c5a172',
'filename': 'BigEarthNet-S1-v1.0.tar.gz',
'directory': 'BigEarthNet-S1-v1.0',
},
's2': {
'url': 'https://zenodo.org/records/12687186/files/BigEarthNet-S2-v1.0.tar.gz',
'md5': '5a64e9ce38deb036a435a7b59494924c',
'filename': 'BigEarthNet-S2-v1.0.tar.gz',
'directory': 'BigEarthNet-v1.0',
},
}
image_size = (120, 120)
[docs] def __init__(
self,
root: Path = 'data',
split: str = 'train',
bands: str = 'all',
num_classes: int = 19,
transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new BigEarthNet dataset instance.
Args:
root: root directory where dataset can be found
split: train/val/test split to load
bands: load Sentinel-1 bands, Sentinel-2, or both. one of {s1, s2, all}
num_classes: number of classes to load in target. one of {19, 43}
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.
"""
assert split in self.splits_metadata
assert bands in ['s1', 's2', 'all']
assert num_classes in [43, 19]
self.root = root
self.split = split
self.bands = bands
self.num_classes = num_classes
self.transforms = transforms
self.download = download
self.checksum = checksum
self.class2idx = {c: i for i, c in enumerate(self.class_sets[43])}
self._verify()
self.folders = self._load_folders()
[docs] def __getitem__(self, index: int) -> dict[str, Tensor]:
"""Return an index within the dataset.
Args:
index: index to return
Returns:
data and label at that index
"""
image = self._load_image(index)
label = self._load_target(index)
sample: dict[str, Tensor] = {'image': image, 'label': label}
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.folders)
def _load_folders(self) -> list[dict[str, str]]:
"""Load folder paths.
Returns:
list of dicts of s1 and s2 folder paths
"""
filename = self.splits_metadata[self.split]['filename']
dir_s1 = self.metadata['s1']['directory']
dir_s2 = self.metadata['s2']['directory']
with open(os.path.join(self.root, filename)) as f:
lines = f.read().strip().splitlines()
pairs = [line.split(',') for line in lines]
folders = [
{
's1': os.path.join(self.root, dir_s1, pair[1]),
's2': os.path.join(self.root, dir_s2, pair[0]),
}
for pair in pairs
]
return folders
def _load_paths(self, index: int) -> list[str]:
"""Load paths to band files.
Args:
index: index to return
Returns:
list of file paths
"""
if self.bands == 'all':
folder_s1 = self.folders[index]['s1']
folder_s2 = self.folders[index]['s2']
paths_s1 = glob.glob(os.path.join(folder_s1, '*.tif'))
paths_s2 = glob.glob(os.path.join(folder_s2, '*.tif'))
paths_s1 = sorted(paths_s1)
paths_s2 = sorted(paths_s2, key=sort_sentinel2_bands)
paths = paths_s1 + paths_s2
elif self.bands == 's1':
folder = self.folders[index]['s1']
paths = glob.glob(os.path.join(folder, '*.tif'))
paths = sorted(paths)
else:
folder = self.folders[index]['s2']
paths = glob.glob(os.path.join(folder, '*.tif'))
paths = sorted(paths, key=sort_sentinel2_bands)
return paths
def _load_image(self, index: int) -> Tensor:
"""Load a single image.
Args:
index: index to return
Returns:
the raster image or target
"""
paths = self._load_paths(index)
images = []
for path in paths:
# Bands are of different spatial resolutions
# Resample to (120, 120)
with rasterio.open(path) as dataset:
array = dataset.read(
indexes=1,
out_shape=self.image_size,
out_dtype='int32',
resampling=Resampling.bilinear,
)
images.append(array)
arrays: np.typing.NDArray[np.int_] = np.stack(images, axis=0)
tensor = torch.from_numpy(arrays).float()
return tensor
def _load_target(self, index: int) -> Tensor:
"""Load the target mask for a single image.
Args:
index: index to return
Returns:
the target label
"""
if self.bands == 's2':
folder = self.folders[index]['s2']
else:
folder = self.folders[index]['s1']
path = glob.glob(os.path.join(folder, '*.json'))[0]
with open(path) as f:
labels = json.load(f)['labels']
# labels -> indices
indices = [self.class2idx[label] for label in labels]
# Map 43 to 19 class labels
if self.num_classes == 19:
indices_optional = [self.label_converter.get(idx) for idx in indices]
indices = [idx for idx in indices_optional if idx is not None]
target = torch.zeros(self.num_classes, dtype=torch.long)
target[indices] = 1
return target
def _verify(self) -> None:
"""Verify the integrity of the dataset."""
keys = ['s1', 's2'] if self.bands == 'all' else [self.bands]
urls = [self.metadata[k]['url'] for k in keys]
md5s = [self.metadata[k]['md5'] for k in keys]
filenames = [self.metadata[k]['filename'] for k in keys]
directories = [self.metadata[k]['directory'] for k in keys]
urls.extend([self.splits_metadata[k]['url'] for k in self.splits_metadata])
md5s.extend([self.splits_metadata[k]['md5'] for k in self.splits_metadata])
filenames_splits = [
self.splits_metadata[k]['filename'] for k in self.splits_metadata
]
filenames.extend(filenames_splits)
# Check if the split file already exist
exists = []
for filename in filenames_splits:
exists.append(os.path.exists(os.path.join(self.root, filename)))
# Check if the files already exist
for directory in directories:
exists.append(os.path.exists(os.path.join(self.root, directory)))
if all(exists):
return
# Check if zip file already exists (if so then extract)
exists = []
for filename in filenames:
filepath = os.path.join(self.root, filename)
if os.path.exists(filepath):
exists.append(True)
self._extract(filepath)
else:
exists.append(False)
if all(exists):
return
# Check if the user requested to download the dataset
if not self.download:
raise DatasetNotFoundError(self)
# Download and extract the dataset
for url, filename, md5 in zip(urls, filenames, md5s):
self._download(url, filename, md5)
filepath = os.path.join(self.root, filename)
self._extract(filepath)
def _download(self, url: str, filename: Path, md5: str) -> None:
"""Download the dataset.
Args:
url: url to download file
filename: output filename to write downloaded file
md5: md5 of downloaded file
"""
download_url(
url, self.root, filename=filename, md5=md5 if self.checksum else None
)
def _extract(self, filepath: Path) -> None:
"""Extract the dataset.
Args:
filepath: path to file to be extracted
"""
if not str(filepath).endswith('.csv'):
extract_archive(filepath)
def _onehot_labels_to_names(
self, label_mask: 'np.typing.NDArray[np.bool_]'
) -> list[str]:
"""Gets a list of class names given a label mask.
Args:
label_mask: a boolean mask corresponding to a set of labels or predictions
Returns:
a list of class names corresponding to the input mask
"""
labels = []
for i, mask in enumerate(label_mask):
if mask:
labels.append(self.class_sets[self.num_classes][i])
return labels
[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
.. versionadded:: 0.2
"""
if self.bands == 's2':
image = np.rollaxis(sample['image'][[3, 2, 1]].numpy(), 0, 3)
image = np.clip(image / 2000, 0, 1)
elif self.bands == 'all':
image = np.rollaxis(sample['image'][[5, 4, 3]].numpy(), 0, 3)
image = np.clip(image / 2000, 0, 1)
elif self.bands == 's1':
image = sample['image'][0].numpy()
label_mask = sample['label'].numpy().astype(np.bool_)
labels = self._onehot_labels_to_names(label_mask)
showing_predictions = 'prediction' in sample
if showing_predictions:
prediction_mask = sample['prediction'].numpy().astype(np.bool_)
predictions = self._onehot_labels_to_names(prediction_mask)
fig, ax = plt.subplots(figsize=(4, 4))
ax.imshow(image)
ax.axis('off')
if show_titles:
title = f'Labels: {", ".join(labels)}'
if showing_predictions:
title += f'\nPredictions: {", ".join(predictions)}'
ax.set_title(title)
if suptitle is not None:
plt.suptitle(suptitle)
return fig
[docs]class BigEarthNetV2(NonGeoDataset):
"""BigEarthNetV2 dataset.
The `BigEarthNet V2 <https://bigearth.net/>`__ dataset contains improved labels, improved
geospatial data splits and additionally pixel-level labels from CORINE Land
Cover (CLC) map of 2018. Additionally, some problematic patches from V1 have been removed.
If you use this dataset in your research, please cite the following paper:
* https://arxiv.org/abs/2407.03653
.. versionadded:: 0.7
"""
class_set = BigEarthNet.class_sets[19]
image_size = BigEarthNet.image_size
url = 'https://hf.co/datasets/torchgeo/bigearthnet/resolve/3cf3a5910a5302d449fdb8e570e5b78de24fe07f/V2/{}'
metadata_locs: ClassVar[dict[str, dict[str, dict[str, str]]]] = {
's1': {
'files': {
'BigEarthNet-S1.tar.gzaa': '039b9ce305fc6582b2c3d60d1573f5b7',
'BigEarthNet-S1.tar.gzab': 'e94f0ea165d04992ca91d8e58e82ec6d',
}
},
's2': {
'files': {
'BigEarthNet-S2.tar.gzaa': '94e8ed32065234d3ab46353d814778d1',
'BigEarthNet-S2.tar.gzab': '24c223d9e36166136c13b24a27debe34',
}
},
'maps': {
'files': {'Reference_Maps.tar.gzaa': 'b0cd1f0a31b49fcbfd61d80f963e759d'}
},
'metadata': {'files': {'metadata.parquet': '55687065e77b6d0b0f1ff604a6e7b49c'}},
}
dir_file_names: ClassVar[dict[str, str]] = {
's1': 'BigEarthNet-S1',
's2': 'BigEarthNet-S2',
'maps': 'Reference_Maps',
'metadata': 'metadata.parquet',
}
# https://collections.sentinel-hub.com/corine-land-cover/readme.html
# Table 1 of https://bigearth.net/static/documents/Description_BigEarthNet_v2.pdf
clc_colors: ClassVar[dict[str, str]] = {
'Urban fabric': '#e6004d',
'Industrial or commercial units': '#cc4df2',
'Arable land': '#ffffa8',
'Permanent crops': '#e68000',
'Pastures': '#e6e64d',
'Complex cultivation patterns': '#ffe64d',
'Land principally occupied by agriculture, with significant areas of natural vegetation': '#e6cc4d',
'Agro-forestry areas': '#f2cca6',
'Broad-leaved forest': '#80ff00',
'Coniferous forest': '#00a600',
'Mixed forest': '#4dff00',
'Natural grassland and sparsely vegetated areas': '#ccf24d',
'Moors, heathland and sclerophyllous vegetation': '#a6ff80',
'Transitional woodland, shrub': '#a6f200',
'Beaches, dunes, sands': '#e6e6e6',
'Inland wetlands': '#a6a6ff',
'Coastal wetlands': '#ccccff',
'Inland waters': '#80f2e6',
'Marine waters': '#e6f2ff',
}
clc_codes: ClassVar[dict[int, int]] = {
111: 0, # Continuous Urban fabric
112: 0, # Discontinuous Urban fabric
121: 1, # Industrial or commercial units
211: 2, # Non-irrigated arable land
212: 2, # Permanently irrigated land
213: 2, # Rice fields
221: 3, # Vineyards
222: 3, # Fruit trees and berry plantations
223: 3, # Olive groves
231: 4, # Pastures
241: 3, # Annual crops with permanent crops
242: 5, # Complex cultivation patterns
243: 6, # Land principally occupied by agriculture...
244: 7, # Agro-forestry areas
311: 8, # Broad-leaved forest
312: 9, # Coniferous forest
313: 10, # Mixed forest
321: 11, # Natural grassland
322: 12, # Moors and heathland
323: 12, # Sclerophyllous vegetation
324: 13, # Transitional woodland/shrub
331: 14, # Beaches, dunes, sands
333: 11, # Sparsely vegetated areas
411: 15, # Inland marshes
412: 15, # Peatbogs
421: 16, # Salt marshes
422: 16, # Salines
511: 17, # Water courses
512: 17, # Water bodies
521: 18, # Coastal lagoons
522: 18, # Estuaries
523: 18, # Sea and ocean
}
valid_splits = ('train', 'val', 'test')
split_map: ClassVar[dict[str, str]] = {
'train': 'train',
'val': 'validation',
'test': 'test',
}
[docs] def __init__(
self,
root: Path = 'data',
split: str = 'train',
bands: str = 'all',
transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new BigEarthNet V2 dataset instance.
Args:
root: root directory where dataset can be found
split: train/val/test split to load
bands: load Sentinel-1 bands, Sentinel-2, or both. one of {s1, s2, all}
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.
AssertionError: If *split*, or *bands*, are not valid.
"""
assert split in self.valid_splits, f'split must be one of {self.valid_splits}'
assert bands in ['s1', 's2', 'all']
self.root = root
self.split = split
self.bands = bands
self.transforms = transforms
self.num_classes = 19
self.download = download
self.checksum = checksum
self.class2idx = {c: i for i, c in enumerate(self.class_set)}
self._verify()
self.metadata_df = pd.read_parquet(os.path.join(self.root, 'metadata.parquet'))
self.metadata_df = self.metadata_df[
self.metadata_df['split'] == self.split_map[self.split]
].reset_index(drop=True)
# Map chosen classes to ordinal numbers, all others mapped to background class
self.ordinal_map = torch.zeros(19)
for corine, ordinal in self.clc_codes.items():
self.ordinal_map[ordinal] = corine
[docs] def __len__(self) -> int:
"""Return the number of data points in the dataset.
Returns:
length of the dataset
"""
return len(self.metadata_df)
[docs] def __getitem__(self, index: int) -> dict[str, Tensor]:
"""Return an index within the dataset.
Args:
index: index to return
Returns:
data and label at that index
"""
sample: dict[str, Tensor] = {}
match self.bands:
case 's1':
sample['image'] = self._load_image(index, 's1')
case 's2':
sample['image'] = self._load_image(index, 's2')
case 'all':
sample['image_s1'] = self._load_image(index, 's1')
sample['image_s2'] = self._load_image(index, 's2')
sample['mask'] = self._load_map(index)
sample['label'] = self._load_target(index)
if self.transforms is not None:
sample = self.transforms(sample)
return sample
def _load_image(self, index: int, sensor: str) -> Tensor:
"""Generic image loader for both S1 and S2.
Args:
index: index to return
sensor: 's1' or 's2'
Returns:
the sensor image
"""
row = self.metadata_df.loc[index]
id_field = 's1_name' if sensor == 's1' else 'patch_id'
patch_id = row[id_field]
if sensor == 's2':
patch_dir = '_'.join(patch_id.split('_')[0:-2])
else:
patch_dir = '_'.join(patch_id.split('_')[0:-3])
paths = glob.glob(
os.path.join(
self.root, self.dir_file_names[sensor], patch_dir, patch_id, '*.tif'
)
)
if sensor == 's2':
paths = sorted(paths, key=sort_sentinel2_bands)
else:
paths = sorted(paths)
images = []
for path in paths:
with rasterio.open(path) as dataset:
array = dataset.read(
indexes=1,
out_shape=self.image_size,
out_dtype='int32',
resampling=Resampling.bilinear,
)
images.append(array)
return torch.from_numpy(np.stack(images, axis=0)).float()
def _load_map(self, index: int) -> Tensor:
"""Load a single image.
Args:
index: index to return
Returns:
the Corine Land Cover map
"""
row = self.metadata_df.loc[index]
patch_id = row['patch_id']
patch_dir = '_'.join(patch_id.split('_')[0:-2])
path = os.path.join(
self.root,
self.dir_file_names['maps'],
patch_dir,
patch_id,
patch_id + '_reference_map.tif',
)
with rasterio.open(path) as dataset:
map = dataset.read(out_dtype='int32')
tensor = torch.from_numpy(map)
# remap to ordinal values
for corine, ordinal in self.clc_codes.items():
tensor[tensor == corine] = ordinal
return tensor.long()
def _load_target(self, index: int) -> Tensor:
"""Load the target mask for a single image.
Args:
index: index to return
Returns:
the target label
"""
label_names = self.metadata_df.iloc[index]['labels']
indices = [self.class2idx[label_names] for label_names in label_names]
image_target = torch.zeros(self.num_classes, dtype=torch.long)
image_target[indices] = 1
return image_target
def _verify(self) -> None:
"""Verify the integrity of the dataset."""
exists = []
for key, metadata in self.metadata_locs.items():
exists.append(
os.path.exists(os.path.join(self.root, self.dir_file_names[key]))
)
if all(exists):
return
# check if compressed files already exist
exists = []
for key, metadata in self.metadata_locs.items():
if key == 'metadata':
exists.append(
os.path.exists(os.path.join(self.root, self.dir_file_names[key]))
)
else:
for fname in metadata['files']:
fpath = os.path.join(self.root, fname)
exists.append(os.path.exists(fpath))
if all(exists):
return
if not self.download:
raise DatasetNotFoundError(self)
self._download()
self._extract()
def _download(self) -> None:
"""Download the required tarball parts using the URL template and md5 sums."""
for key, meta in self.metadata_locs.items():
for fname, md5 in meta['files'].items():
target_path = os.path.join(self.root, fname)
if not os.path.exists(target_path):
download_url(self.url.format(fname), self.root, md5)
def _extract(self) -> None:
"""Extract the tarball parts.
For each modality (s1, s2, maps), its parts are concatenated together and then extracted.
"""
chunk_size = 2**15 # same as used in torchvision and ssl4eo
for key, meta in self.metadata_locs.items():
if key == 'metadata':
continue
parts = [os.path.join(self.root, f) for f in meta['files'].keys()]
concat_path = os.path.join(self.root, self.dir_file_names[key] + '.tar.gz')
with open(concat_path, 'wb') as outfile:
for part in parts:
with open(part, 'rb') as g:
while chunk := g.read(chunk_size):
outfile.write(chunk)
extract_archive(concat_path, self.root)
[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(1, 2 if self.bands != 'all' else 3, figsize=(12, 4))
if self.bands in ['s2', 'all']:
s2_img = sample['image_s2' if self.bands == 'all' else 'image']
rgb = np.rollaxis(s2_img[[3, 2, 1]].numpy(), 0, 3)
axes[0].imshow(np.clip(rgb / 2000, 0, 1))
if show_titles:
axes[0].set_title('Sentinel-2 RGB')
axes[0].axis('off')
if self.bands in ['s1', 'all']:
idx = 0 if self.bands == 's1' else 1
s1_img = sample['image_s1' if self.bands == 'all' else 'image']
axes[idx].imshow(s1_img[0].numpy())
if show_titles:
axes[idx].set_title('Sentinel-1 VV')
axes[idx].axis('off')
# Handle mask plotting
mask_idx = 1 if self.bands != 'all' else 2
mask = sample['mask'][0].numpy()
# Get unique ordinal labels from mask
unique_labels = sorted(np.unique(mask))
# Map ordinal labels to class names and colors directly
colors = []
class_names = []
for label in unique_labels:
name = self.class_set[label] # Get class name from ordinal index
colors.append(self.clc_colors[name]) # Get color for class name
class_names.append(name)
# Create custom colormap
cmap = ListedColormap(colors)
bounds = [*unique_labels, unique_labels[-1] + 1]
norm = BoundaryNorm(bounds, len(colors))
axes[mask_idx].imshow(mask, cmap=cmap, norm=norm)
# Add legend with class names
legend_elements = [Rectangle((0, 0), 1, 1, facecolor=color) for color in colors]
wrapped_names = [textwrap.fill(name, width=25) for name in class_names]
axes[mask_idx].legend(
legend_elements,
wrapped_names,
loc='center left',
bbox_to_anchor=(1, 0.5),
fontsize='x-small',
)
axes[mask_idx].axis('off')
if show_titles:
axes[mask_idx].set_title('Land Cover Map')
if 'label' in sample:
label_indices = sample['label'].nonzero().squeeze(1).tolist()
label_names = [self.class_set[idx] for idx in label_indices]
if suptitle:
suptitle = f'{suptitle}\nLabels: {", ".join(label_names)}'
else:
suptitle = f'Labels: {", ".join(label_names)}'
if suptitle:
plt.suptitle(suptitle)
# Adjust layout to prevent overlap
plt.tight_layout()
return fig
