Source code for torchgeo.datasets.copernicus.lc100seg_s3
# Copyright (c) TorchGeo Contributors. All rights reserved.
# Licensed under the MIT License.
"""Copernicus-Bench LC100Seg-S3 dataset."""
import glob
import os
from collections.abc import Callable, Sequence
from typing import ClassVar, Literal
import pandas as pd
from matplotlib.colors import ListedColormap
from torch import Tensor
from ..utils import Path, stack_samples
from .base import CopernicusBenchBase
[docs]class CopernicusBenchLC100SegS3(CopernicusBenchBase):
"""Copernicus-Bench LC100Seg-S3 dataset.
LC100Seg-S3 is a multilabel land use/land cover segmentation dataset based on
Sentinel-3 OLCI images and CGLS-LC100 land cover maps. CGLS-LC100 is a product in
the Copernicus Global Land Service (CGLS) portfolio and delivers a global 23-class
land cover map at 100 m spatial resolution.
This benchmark supports both static (1 image/location) and time series
(1-4 images/location) modes, the former is used in the original benchmark.
.. list-table:: Classes
:header-rows: 1
* - Value
- Description
* - 0
- Unknown. No or not enough satellite data available.
* - 20
- Shrubs. Woody perennial plants with persistent and woody stems and without
any defined main stem being less than 5 m tall. The shrub foliage can be
either evergreen or deciduous.
* - 30
- Herbaceous vegetation. Plants without persistent stem or shoots above ground
and lacking definite firm structure. Tree and shrub cover is less than 10 %.
* - 40
- Cultivated and managed vegetation / agriculture. Lands covered with temporary
crops followed by harvest and a bare soil period (e.g., single and multiple
cropping systems). Note that perennial woody crops will be classified as the
appropriate forest or shrub land cover type.
* - 50
- Urban / built up. Land covered by buildings and other man-made structures.
* - 60
- Bare / sparse vegetation. Lands with exposed soil, sand, or rocks and never
has more than 10 % vegetated cover during any time of the year.
* - 70
- Snow and ice. Lands under snow or ice cover throughout the year.
* - 80
- Permanent water bodies. Lakes, reservoirs, and rivers. Can be either fresh
or salt-water bodies.
* - 90
- Herbaceous wetland. Lands with a permanent mixture of water and herbaceous
or woody vegetation. The vegetation can be present in either salt, brackish,
or fresh water.
* - 100
- Moss and lichen.
* - 111
- Closed forest, evergreen needle leaf. Tree canopy >70 %, almost all needle
leaf trees remain green all year. Canopy is never without green foliage.
* - 112
- Closed forest, evergreen broad leaf. Tree canopy >70 %, almost all broadleaf
trees remain green year round. Canopy is never without green foliage.
* - 113
- Closed forest, deciduous needle leaf. Tree canopy >70 %, consists of seasonal
needle leaf tree communities with an annual cycle of leaf-on and leaf-off
periods.
* - 114
- Closed forest, deciduous broad leaf. Tree canopy >70 %, consists of seasonal
broadleaf tree communities with an annual cycle of leaf-on and leaf-off
periods.
* - 115
- Closed forest, mixed.
* - 116
- Closed forest, not matching any of the other definitions.
* - 121
- Open forest, evergreen needle leaf. Top layer- trees 15-70 % and second
layer- mixed of shrubs and grassland, almost all needle leaf trees remain
green all year. Canopy is never without green foliage.
* - 122
- Open forest, evergreen broad leaf. Top layer- trees 15-70 % and second layer-
mixed of shrubs and grassland, almost all broadleaf trees remain green year
round. Canopy is never without green foliage.
* - 123
- Open forest, deciduous needle leaf. Top layer- trees 15-70 % and second
layer- mixed of shrubs and grassland, consists of seasonal needle leaf tree
communities with an annual cycle of leaf-on and leaf-off periods.
* - 124
- Open forest, deciduous broad leaf. Top layer- trees 15-70 % and second layer-
mixed of shrubs and grassland, consists of seasonal broadleaf tree
communities with an annual cycle of leaf-on and leaf-off periods.
* - 125
- Open forest, mixed.
* - 126
- Open forest, not matching any of the other definitions.
* - 200
- Oceans, seas. Can be either fresh or salt-water bodies.
If you use this dataset in your research, please cite the following papers:
* https://arxiv.org/abs/2503.11849
* https://doi.org/10.5281/zenodo.3939049
.. versionadded:: 0.7
"""
url = 'https://hf.co/datasets/wangyi111/Copernicus-Bench/resolve/9d252acd3aa0e3da3128e05c6f028647f0e48e5f/l2_lc100_s3/lc100_s3.zip'
md5 = '967d1da6286e0d0e346e425a8f3800e9'
zipfile = 'lc100_s3.zip'
filename = 'multilabel-{}.csv'
directory = 'lc100_s3'
filename_regex = r'S3[AB]_(?P<date>\d{8}T\d{6})'
all_bands = (
'Oa01_radiance',
'Oa02_radiance',
'Oa03_radiance',
'Oa04_radiance',
'Oa05_radiance',
'Oa06_radiance',
'Oa07_radiance',
'Oa08_radiance',
'Oa09_radiance',
'Oa10_radiance',
'Oa11_radiance',
'Oa12_radiance',
'Oa13_radiance',
'Oa14_radiance',
'Oa15_radiance',
'Oa16_radiance',
'Oa17_radiance',
'Oa18_radiance',
'Oa19_radiance',
'Oa20_radiance',
'Oa21_radiance',
)
rgb_bands = ('Oa08_radiance', 'Oa06_radiance', 'Oa04_radiance')
cmap = ListedColormap(
[
'#282828',
'#ffbb22',
'#ffff4c',
'#f096ff',
'#fa0000',
'#b4b4b4',
'#f0f0f0',
'#0032c8',
'#0096a0',
'#fae6a0',
'#58481f',
'#009900',
'#70663e',
'#00cc00',
'#4e751f',
'#007800',
'#666000',
'#8db400',
'#8d7400',
'#a0dc00',
'#929900',
'#648c00',
'#000080',
]
)
classes = (
'Unknown',
'Shrubs',
'Herbaceous vegetation',
'Cultivated and managed vegetation / agriculture',
'Urban / built up',
'Bare / sparse vegetation',
'Snow and ice',
'Permanent water bodies',
'Herbaceous wetland',
'Moss and lichen',
'Closed forest, evergreen needle leaf',
'Closed forest, evergreen broad leaf',
'Closed forest, deciduous needle leaf',
'Closed forest, deciduous broad leaf',
'Closed forest, mixed',
'Closed forest, not matching any of the other definitions',
'Open forest, evergreen needle leaf',
'Open forest, evergreen broad leaf',
'Open forest, deciduous needle leaf',
'Open forest, deciduous broad leaf',
'Open forest, mixed',
'Open forest, not matching any of the other definitions',
'Oceans, seas',
)
class_map: ClassVar[dict[int, int]] = {
0: 0,
20: 1,
30: 2,
40: 3,
50: 4,
60: 5,
70: 6,
80: 7,
90: 8,
100: 9,
111: 10,
112: 11,
113: 12,
114: 13,
115: 14,
116: 15,
121: 16,
122: 17,
123: 18,
124: 19,
125: 20,
126: 21,
200: 22,
}
[docs] def __init__(
self,
root: Path = 'data',
split: Literal['train', 'val', 'test'] = 'train',
mode: Literal['static', 'time-series'] = 'static',
bands: Sequence[str] | None = None,
transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new CopernicusBenchLC100SegS3 instance.
Args:
root: Root directory where dataset can be found.
split: One of 'train', 'val', or 'test'.
mode: One of 'static' or 'time-series'.
bands: Sequence of band names to load (defaults to all bands).
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.
"""
self.mode = mode
super().__init__(root, split, bands, transforms, download, checksum)
filepath = os.path.join(root, self.directory, self.filename.format(split))
self.files = pd.read_csv(filepath)
if mode == 'static':
filepath = os.path.join(root, self.directory, f'static_fnames-{split}.csv')
self.static_files = pd.read_csv(filepath, header=None)
[docs] def __getitem__(self, index: int) -> dict[str, Tensor]:
"""Return an index within the dataset.
Args:
index: Index to return.
Returns:
Data and labels at that index.
"""
row = self.files.iloc[index].values
match self.mode:
case 'static':
pid, file = self.static_files.iloc[index]
path = os.path.join(self.root, self.directory, 's3_olci', pid, file)
sample = self._load_image(path)
case 'time-series':
pid = row[0]
paths = os.path.join(self.root, self.directory, 's3_olci', pid, '*.tif')
samples = [self._load_image(path) for path in sorted(glob.glob(paths))]
sample = stack_samples(samples)
path = os.path.join(self.root, self.directory, 'lc100', f'{pid}.tif')
sample |= self._load_mask(path)
for before, after in self.class_map.items():
sample['mask'][sample['mask'] == before] = after
if self.transforms is not None:
sample = self.transforms(sample)
return sample
