Ruff: enable ruff-specific rules (#2218)

* Ruff: enable ruff-specific rules

* Static class variables

* String colormap must be list

* String colormap must be list

docs/conf.py

@@ -19,7 +19,7 @@ import pytorch_sphinx_theme
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 sys.path.insert(0, os.path.abspath('..'))
 
-import torchgeo  # noqa: E402
+import torchgeo
 
 # -- Project information -----------------------------------------------------
 

docs/tutorials/custom_raster_dataset.ipynb

@@ -369,8 +369,8 @@
     "    date_format = '%Y%m%dT%H%M%S'\n",
     "    is_image = True\n",
     "    separate_files = True\n",
-    "    all_bands = ['B02', 'B03', 'B04', 'B08']\n",
+    "    all_bands = ('B02', 'B03', 'B04', 'B08')\n",
-    "    rgb_bands = ['B04', 'B03', 'B02']"
+    "    rgb_bands = ('B04', 'B03', 'B02')"
    ]
   },
   {
@@ -432,8 +432,8 @@
     "    date_format = '%Y%m%dT%H%M%S'\n",
     "    is_image = True\n",
     "    separate_files = True\n",
-    "    all_bands = ['B02', 'B03', 'B04', 'B08']\n",
+    "    all_bands = ('B02', 'B03', 'B04', 'B08')\n",
-    "    rgb_bands = ['B04', 'B03', 'B02']\n",
+    "    rgb_bands = ('B04', 'B03', 'B02')\n",
     "\n",
     "    def plot(self, sample):\n",
     "        # Find the correct band index order\n",

experiments/ssl4eo/download_ssl4eo.py

@@ -125,7 +125,7 @@ def filter_collection(
 
     if filtered.size().getInfo() == 0:
         raise ee.EEException(
-            f'ImageCollection.filter: No suitable images found in ({coords[1]:.4f}, {coords[0]:.4f}) between {period[0]} and {period[1]}.'  # noqa: E501
+            f'ImageCollection.filter: No suitable images found in ({coords[1]:.4f}, {coords[0]:.4f}) between {period[0]} and {period[1]}.'
         )
     return filtered
 

experiments/ssl4eo/sample_ssl4eo.py

@@ -47,7 +47,7 @@ from tqdm import tqdm
 def get_world_cities(
     download_root: str = 'world_cities', size: int = 10000
 ) -> pd.DataFrame:
-    url = 'https://simplemaps.com/static/data/world-cities/basic/simplemaps_worldcities_basicv1.71.zip'  # noqa: E501
+    url = 'https://simplemaps.com/static/data/world-cities/basic/simplemaps_worldcities_basicv1.71.zip'
     filename = 'worldcities.csv'
     download_and_extract_archive(url, download_root)
     cols = ['city', 'lat', 'lng', 'population']

pyproject.toml

@@ -268,7 +268,7 @@ quote-style = "single"
 skip-magic-trailing-comma = true
 
 [tool.ruff.lint]
-extend-select = ["ANN", "D", "I", "NPY201", "UP"]
+extend-select = ["ANN", "D", "I", "NPY201", "RUF", "UP"]
 ignore = ["ANN101", "ANN102", "ANN401"]
 
 [tool.ruff.lint.per-file-ignores]

tests/data/dfc2022/data.py

@@ -19,36 +19,36 @@ np.random.seed(0)
 
 train_set = [
     {
-        'image': 'labeled_train/Nantes_Saint-Nazaire/BDORTHO/44-2013-0295-6713-LA93-0M50-E080.tif',  # noqa: E501
+        'image': 'labeled_train/Nantes_Saint-Nazaire/BDORTHO/44-2013-0295-6713-LA93-0M50-E080.tif',
-        'dem': 'labeled_train/Nantes_Saint-Nazaire/RGEALTI/44-2013-0295-6713-LA93-0M50-E080_RGEALTI.tif',  # noqa: E501
+        'dem': 'labeled_train/Nantes_Saint-Nazaire/RGEALTI/44-2013-0295-6713-LA93-0M50-E080_RGEALTI.tif',
-        'target': 'labeled_train/Nantes_Saint-Nazaire/UrbanAtlas/44-2013-0295-6713-LA93-0M50-E080_UA2012.tif',  # noqa: E501
+        'target': 'labeled_train/Nantes_Saint-Nazaire/UrbanAtlas/44-2013-0295-6713-LA93-0M50-E080_UA2012.tif',
     },
     {
-        'image': 'labeled_train/Nice/BDORTHO/06-2014-1007-6318-LA93-0M50-E080.tif',  # noqa: E501
+        'image': 'labeled_train/Nice/BDORTHO/06-2014-1007-6318-LA93-0M50-E080.tif',
-        'dem': 'labeled_train/Nice/RGEALTI/06-2014-1007-6318-LA93-0M50-E080_RGEALTI.tif',  # noqa: E501
+        'dem': 'labeled_train/Nice/RGEALTI/06-2014-1007-6318-LA93-0M50-E080_RGEALTI.tif',
-        'target': 'labeled_train/Nice/UrbanAtlas/06-2014-1007-6318-LA93-0M50-E080_UA2012.tif',  # noqa: E501
+        'target': 'labeled_train/Nice/UrbanAtlas/06-2014-1007-6318-LA93-0M50-E080_UA2012.tif',
     },
 ]
 
 unlabeled_set = [
     {
-        'image': 'unlabeled_train/Calais_Dunkerque/BDORTHO/59-2012-0650-7077-LA93-0M50-E080.tif',  # noqa: E501
+        'image': 'unlabeled_train/Calais_Dunkerque/BDORTHO/59-2012-0650-7077-LA93-0M50-E080.tif',
-        'dem': 'unlabeled_train/Calais_Dunkerque/RGEALTI/59-2012-0650-7077-LA93-0M50-E080_RGEALTI.tif',  # noqa: E501
+        'dem': 'unlabeled_train/Calais_Dunkerque/RGEALTI/59-2012-0650-7077-LA93-0M50-E080_RGEALTI.tif',
     },
     {
-        'image': 'unlabeled_train/LeMans/BDORTHO/72-2013-0469-6789-LA93-0M50-E080.tif',  # noqa: E501
+        'image': 'unlabeled_train/LeMans/BDORTHO/72-2013-0469-6789-LA93-0M50-E080.tif',
-        'dem': 'unlabeled_train/LeMans/RGEALTI/72-2013-0469-6789-LA93-0M50-E080_RGEALTI.tif',  # noqa: E501
+        'dem': 'unlabeled_train/LeMans/RGEALTI/72-2013-0469-6789-LA93-0M50-E080_RGEALTI.tif',
     },
 ]
 
 val_set = [
     {
-        'image': 'val/Marseille_Martigues/BDORTHO/13-2014-0900-6268-LA93-0M50-E080.tif',  # noqa: E501
+        'image': 'val/Marseille_Martigues/BDORTHO/13-2014-0900-6268-LA93-0M50-E080.tif',
-        'dem': 'val/Marseille_Martigues/RGEALTI/13-2014-0900-6268-LA93-0M50-E080_RGEALTI.tif',  # noqa: E501
+        'dem': 'val/Marseille_Martigues/RGEALTI/13-2014-0900-6268-LA93-0M50-E080_RGEALTI.tif',
     },
     {
-        'image': 'val/Clermont-Ferrand/BDORTHO/63-2013-0711-6530-LA93-0M50-E080.tif',  # noqa: E501
+        'image': 'val/Clermont-Ferrand/BDORTHO/63-2013-0711-6530-LA93-0M50-E080.tif',
-        'dem': 'val/Clermont-Ferrand/RGEALTI/63-2013-0711-6530-LA93-0M50-E080_RGEALTI.tif',  # noqa: E501
+        'dem': 'val/Clermont-Ferrand/RGEALTI/63-2013-0711-6530-LA93-0M50-E080_RGEALTI.tif',
     },
 ]
 

tests/data/seasonet/data.py

@@ -112,7 +112,7 @@ for season in seasons:
     # Compute checksums
     with open(archive, 'rb') as f:
         md5 = hashlib.md5(f.read()).hexdigest()
-        print(f'{season}: {repr(md5)}')
+        print(f'{season}: {md5!r}')
 
 # Write meta.csv
 with open('meta.csv', 'w') as f:
@@ -121,7 +121,7 @@ with open('meta.csv', 'w') as f:
 # Compute checksums
 with open('meta.csv', 'rb') as f:
     md5 = hashlib.md5(f.read()).hexdigest()
-    print(f'meta.csv: {repr(md5)}')
+    print(f'meta.csv: {md5!r}')
 
 os.makedirs('splits', exist_ok=True)
 
@@ -138,4 +138,4 @@ shutil.make_archive('splits', 'zip', '.', 'splits')
 # Compute checksums
 with open('splits.zip', 'rb') as f:
     md5 = hashlib.md5(f.read()).hexdigest()
-    print(f'splits: {repr(md5)}')
+    print(f'splits: {md5!r}')

tests/datasets/test_eurocrops.py

@@ -83,5 +83,5 @@ class TestEuroCrops:
             dataset[query]
 
     def test_integrity_error(self, dataset: EuroCrops) -> None:
-        dataset.zenodo_files = [('AA.zip', 'invalid')]
+        dataset.zenodo_files = (('AA.zip', 'invalid'),)
         assert not dataset._check_integrity()

tests/datasets/test_geo.py

@@ -72,7 +72,7 @@ class CustomVectorDataset(VectorDataset):
 
 
 class CustomSentinelDataset(Sentinel2):
-    all_bands: list[str] = []
+    all_bands: tuple[str, ...] = ()
     separate_files = False
 
 
@@ -356,7 +356,7 @@ class TestRasterDataset:
 
     def test_no_all_bands(self) -> None:
         root = os.path.join('tests', 'data', 'sentinel2')
-        bands = ['B04', 'B03', 'B02']
+        bands = ('B04', 'B03', 'B02')
         transforms = nn.Identity()
         cache = True
         msg = (

tests/trainers/test_byol.py

@@ -73,7 +73,7 @@ class TestBYOLTask:
             '1',
         ]
 
-        main(['fit'] + args)
+        main(['fit', *args])
 
     @pytest.fixture
     def weights(self) -> WeightsEnum:

tests/trainers/test_classification.py

@@ -103,13 +103,13 @@ class TestClassificationTask:
             '1',
         ]
 
-        main(['fit'] + args)
+        main(['fit', *args])
         try:
-            main(['test'] + args)
+            main(['test', *args])
         except MisconfigurationException:
             pass
         try:
-            main(['predict'] + args)
+            main(['predict', *args])
         except MisconfigurationException:
             pass
 
@@ -259,13 +259,13 @@ class TestMultiLabelClassificationTask:
             '1',
         ]
 
-        main(['fit'] + args)
+        main(['fit', *args])
         try:
-            main(['test'] + args)
+            main(['test', *args])
         except MisconfigurationException:
             pass
         try:
-            main(['predict'] + args)
+            main(['predict', *args])
         except MisconfigurationException:
             pass
 

tests/trainers/test_detection.py

@@ -97,13 +97,13 @@ class TestObjectDetectionTask:
             '1',
         ]
 
-        main(['fit'] + args)
+        main(['fit', *args])
         try:
-            main(['test'] + args)
+            main(['test', *args])
         except MisconfigurationException:
             pass
         try:
-            main(['predict'] + args)
+            main(['predict', *args])
         except MisconfigurationException:
             pass
 

tests/trainers/test_iobench.py

@@ -27,12 +27,12 @@ class TestClassificationTask:
             '1',
         ]
 
-        main(['fit'] + args)
+        main(['fit', *args])
         try:
-            main(['test'] + args)
+            main(['test', *args])
         except MisconfigurationException:
             pass
         try:
-            main(['predict'] + args)
+            main(['predict', *args])
         except MisconfigurationException:
             pass

tests/trainers/test_moco.py

@@ -63,7 +63,7 @@ class TestMoCoTask:
             '1',
         ]
 
-        main(['fit'] + args)
+        main(['fit', *args])
 
     def test_version_warnings(self) -> None:
         with pytest.warns(UserWarning, match='MoCo v1 uses a memory bank'):

tests/trainers/test_regression.py

@@ -84,13 +84,13 @@ class TestRegressionTask:
             '1',
         ]
 
-        main(['fit'] + args)
+        main(['fit', *args])
         try:
-            main(['test'] + args)
+            main(['test', *args])
         except MisconfigurationException:
             pass
         try:
-            main(['predict'] + args)
+            main(['predict', *args])
         except MisconfigurationException:
             pass
 
@@ -237,13 +237,13 @@ class TestPixelwiseRegressionTask:
             '1',
         ]
 
-        main(['fit'] + args)
+        main(['fit', *args])
         try:
-            main(['test'] + args)
+            main(['test', *args])
         except MisconfigurationException:
             pass
         try:
-            main(['predict'] + args)
+            main(['predict', *args])
         except MisconfigurationException:
             pass
 

tests/trainers/test_segmentation.py

@@ -108,13 +108,13 @@ class TestSemanticSegmentationTask:
             '1',
         ]
 
-        main(['fit'] + args)
+        main(['fit', *args])
         try:
-            main(['test'] + args)
+            main(['test', *args])
         except MisconfigurationException:
             pass
         try:
-            main(['predict'] + args)
+            main(['predict', *args])
         except MisconfigurationException:
             pass
 

tests/trainers/test_simclr.py

@@ -63,7 +63,7 @@ class TestSimCLRTask:
             '1',
         ]
 
-        main(['fit'] + args)
+        main(['fit', *args])
 
     def test_version_warnings(self) -> None:
         with pytest.warns(UserWarning, match='SimCLR v1 only uses 2 layers'):

torchgeo/datamodules/seco.py

@@ -37,7 +37,7 @@ class SeasonalContrastS2DataModule(NonGeoDataModule):
         seasons = kwargs.get('seasons', 1)
 
         # Normalization only available for RGB dataset, defined here:
-        # https://github.com/ServiceNow/seasonal-contrast/blob/8285173ec205b64bc3e53b880344dd6c3f79fa7a/datasets/seco_dataset.py  # noqa: E501
+        # https://github.com/ServiceNow/seasonal-contrast/blob/8285173ec205b64bc3e53b880344dd6c3f79fa7a/datasets/seco_dataset.py
         if bands == SeasonalContrastS2.rgb_bands:
             _min = torch.tensor([3, 2, 0])
             _max = torch.tensor([88, 103, 129])

torchgeo/datamodules/so2sat.py

@@ -3,7 +3,7 @@
 
 """So2Sat datamodule."""
 
-from typing import Any
+from typing import Any, ClassVar
 
 import torch
 from torch import Generator, Tensor
@@ -21,7 +21,7 @@ class So2SatDataModule(NonGeoDataModule):
     "train" set and use the "test" set as the test set.
     """
 
-    means_per_version: dict[str, Tensor] = {
+    means_per_version: ClassVar[dict[str, Tensor]] = {
         '2': torch.tensor(
             [
                 -0.00003591224260,
@@ -91,7 +91,7 @@ class So2SatDataModule(NonGeoDataModule):
     }
     means_per_version['3_culture_10'] = means_per_version['2']
 
-    stds_per_version: dict[str, Tensor] = {
+    stds_per_version: ClassVar[dict[str, Tensor]] = {
         '2': torch.tensor(
             [
                 0.17555201,

torchgeo/datamodules/ssl4eo.py

@@ -45,7 +45,7 @@ class SSL4EOS12DataModule(NonGeoDataModule):
     .. versionadded:: 0.5
     """
 
-    # https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/datasets/EuroSat/eurosat_dataset.py#L97  # noqa: E501
+    # https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/datasets/EuroSat/eurosat_dataset.py#L97
     mean = torch.tensor(0)
     std = torch.tensor(10000)
 

torchgeo/datasets/advance.py

@@ -63,14 +63,14 @@ class ADVANCE(NonGeoDataset):
         * `scipy <https://pypi.org/project/scipy/>`_ to load the audio files to tensors
     """
 
-    urls = [
+    urls = (
         'https://zenodo.org/record/3828124/files/ADVANCE_vision.zip?download=1',
         'https://zenodo.org/record/3828124/files/ADVANCE_sound.zip?download=1',
-    ]
+    )
-    filenames = ['ADVANCE_vision.zip', 'ADVANCE_sound.zip']
+    filenames = ('ADVANCE_vision.zip', 'ADVANCE_sound.zip')
-    md5s = ['a9e8748219ef5864d3b5a8979a67b471', 'a2d12f2d2a64f5c3d3a9d8c09aaf1c31']
+    md5s = ('a9e8748219ef5864d3b5a8979a67b471', 'a2d12f2d2a64f5c3d3a9d8c09aaf1c31')
-    directories = ['vision', 'sound']
+    directories = ('vision', 'sound')
-    classes = [
+    classes: tuple[str, ...] = (
         'airport',
         'beach',
         'bridge',
@@ -84,7 +84,7 @@ class ADVANCE(NonGeoDataset):
         'sparse shrub land',
         'sports land',
         'train station',
-    ]
+    )
 
     def __init__(
         self,
@@ -119,7 +119,7 @@ class ADVANCE(NonGeoDataset):
             raise DatasetNotFoundError(self)
 
         self.files = self._load_files(self.root)
-        self.classes = sorted({f['cls'] for f in self.files})
+        self.classes = tuple(sorted({f['cls'] for f in self.files}))
         self.class_to_idx: dict[str, int] = {c: i for i, c in enumerate(self.classes)}
 
     def __getitem__(self, index: int) -> dict[str, Tensor]:

torchgeo/datasets/agb_live_woody_density.py

@@ -46,7 +46,7 @@ class AbovegroundLiveWoodyBiomassDensity(RasterDataset):
 
     is_image = False
 
-    url = 'https://opendata.arcgis.com/api/v3/datasets/e4bdbe8d6d8d4e32ace7d36a4aec7b93_0/downloads/data?format=geojson&spatialRefId=4326'  # noqa: E501
+    url = 'https://opendata.arcgis.com/api/v3/datasets/e4bdbe8d6d8d4e32ace7d36a4aec7b93_0/downloads/data?format=geojson&spatialRefId=4326'
 
     base_filename = 'Aboveground_Live_Woody_Biomass_Density.geojson'
 

torchgeo/datasets/agrifieldnet.py

@@ -7,7 +7,7 @@ import os
 import pathlib
 import re
 from collections.abc import Callable, Iterable, Sequence
-from typing import Any, cast
+from typing import Any, ClassVar, cast
 
 import matplotlib.pyplot as plt
 import torch
@@ -90,8 +90,8 @@ class AgriFieldNet(RasterDataset):
         _(?P<band>B[0-9A-Z]{2})_10m
     """
 
-    rgb_bands = ['B04', 'B03', 'B02']
+    rgb_bands = ('B04', 'B03', 'B02')
-    all_bands = [
+    all_bands = (
         'B01',
         'B02',
         'B03',
@@ -104,9 +104,9 @@ class AgriFieldNet(RasterDataset):
         'B09',
         'B11',
         'B12',
-    ]
+    )
 
-    cmap = {
+    cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {
         0: (0, 0, 0, 255),
         1: (255, 211, 0, 255),
         2: (255, 37, 37, 255),

torchgeo/datasets/airphen.py

@@ -40,8 +40,8 @@ class Airphen(RasterDataset):
 
     # Each camera measures a custom set of spectral bands chosen at purchase time.
     # Hiphen offers 8 bands to choose from, sorted from short to long wavelength.
-    all_bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8']
+    all_bands = ('B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8')
-    rgb_bands = ['B4', 'B3', 'B1']
+    rgb_bands = ('B4', 'B3', 'B1')
 
     def plot(
         self,

torchgeo/datasets/benin_cashews.py

@@ -147,7 +147,7 @@ class BeninSmallHolderCashews(NonGeoDataset):
     )
     rgb_bands = ('B04', 'B03', 'B02')
 
-    classes = [
+    classes = (
         'No data',
         'Well-managed planatation',
         'Poorly-managed planatation',
@@ -155,7 +155,7 @@ class BeninSmallHolderCashews(NonGeoDataset):
         'Residential',
         'Background',
         'Uncertain',
-    ]
+    )
 
     # Same for all tiles
     tile_height = 1186
@@ -199,11 +199,13 @@ class BeninSmallHolderCashews(NonGeoDataset):
 
         # Calculate the indices that we will use over all tiles
         self.chips_metadata = []
-        for y in list(range(0, self.tile_height - self.chip_size, stride)) + [
+        for y in [
-            self.tile_height - self.chip_size
+            *list(range(0, self.tile_height - self.chip_size, stride)),
+            self.tile_height - self.chip_size,
         ]:
-            for x in list(range(0, self.tile_width - self.chip_size, stride)) + [
+            for x in [
-                self.tile_width - self.chip_size
+                *list(range(0, self.tile_width - self.chip_size, stride)),
+                self.tile_width - self.chip_size,
             ]:
                 self.chips_metadata.append((y, x))
 

torchgeo/datasets/bigearthnet.py

@@ -7,6 +7,7 @@ import glob
 import json
 import os
 from collections.abc import Callable
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -124,9 +125,9 @@ class BigEarthNet(NonGeoDataset):
 
     * https://doi.org/10.1109/IGARSS.2019.8900532
 
-    """  # noqa: E501
+    """
 
-    class_sets = {
+    class_sets: ClassVar[dict[int, list[str]]] = {
         19: [
             'Urban fabric',
             'Industrial or commercial units',
@@ -197,7 +198,7 @@ class BigEarthNet(NonGeoDataset):
         ],
     }
 
-    label_converter = {
+    label_converter: ClassVar[dict[int, int]] = {
         0: 0,
         1: 0,
         2: 1,
@@ -232,24 +233,24 @@ class BigEarthNet(NonGeoDataset):
         42: 18,
     }
 
-    splits_metadata = {
+    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',  # noqa: E501
+            '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',  # noqa: E501
+            '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',  # noqa: E501
+            '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 = {
+    metadata: ClassVar[dict[str, dict[str, str]]] = {
         's1': {
             'url': 'https://zenodo.org/records/12687186/files/BigEarthNet-S1-v1.0.tar.gz',
             'md5': '94ced73440dea8c7b9645ee738c5a172',

torchgeo/datasets/biomassters.py

@@ -50,7 +50,7 @@ class BioMassters(NonGeoDataset):
     .. versionadded:: 0.5
     """
 
-    valid_splits = ['train', 'test']
+    valid_splits = ('train', 'test')
     valid_sensors = ('S1', 'S2')
 
     metadata_filename = 'The_BioMassters_-_features_metadata.csv.csv'

torchgeo/datasets/cbf.py

@@ -30,7 +30,7 @@ class CanadianBuildingFootprints(VectorDataset):
     # https://github.com/microsoft/CanadianBuildingFootprints/issues/11
 
     url = 'https://usbuildingdata.blob.core.windows.net/canadian-buildings-v2/'
-    provinces_territories = [
+    provinces_territories = (
         'Alberta',
         'BritishColumbia',
         'Manitoba',
@@ -44,8 +44,8 @@ class CanadianBuildingFootprints(VectorDataset):
         'Quebec',
         'Saskatchewan',
         'YukonTerritory',
-    ]
+    )
-    md5s = [
+    md5s = (
         '8b4190424e57bb0902bd8ecb95a9235b',
         'fea05d6eb0006710729c675de63db839',
         'adf11187362624d68f9c69aaa693c46f',
@@ -59,7 +59,7 @@ class CanadianBuildingFootprints(VectorDataset):
         '9ff4417ae00354d39a0cf193c8df592c',
         'a51078d8e60082c7d3a3818240da6dd5',
         'c11f3bd914ecabd7cac2cb2871ec0261',
-    ]
+    )
 
     def __init__(
         self,

torchgeo/datasets/cdl.py

@@ -6,7 +6,7 @@
 import os
 import pathlib
 from collections.abc import Callable, Iterable
-from typing import Any
+from typing import Any, ClassVar
 
 import matplotlib.pyplot as plt
 import torch
@@ -38,7 +38,7 @@ class CDL(RasterDataset):
     If you use this dataset in your research, please cite it using the following format:
 
     * https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php#Section1_14.0
-    """  # noqa: E501
+    """
 
     filename_glob = '*_30m_cdls.tif'
     filename_regex = r"""
@@ -49,8 +49,8 @@ class CDL(RasterDataset):
     date_format = '%Y'
     is_image = False
 
-    url = 'https://www.nass.usda.gov/Research_and_Science/Cropland/Release/datasets/{}_30m_cdls.zip'  # noqa: E501
+    url = 'https://www.nass.usda.gov/Research_and_Science/Cropland/Release/datasets/{}_30m_cdls.zip'
-    md5s = {
+    md5s: ClassVar[dict[int, str]] = {
         2023: '8c7685d6278d50c554f934b16a6076b7',
         2022: '754cf50670cdfee511937554785de3e6',
         2021: '27606eab08fe975aa138baad3e5dfcd8',
@@ -69,7 +69,7 @@ class CDL(RasterDataset):
         2008: '0610f2f17ab60a9fbb3baeb7543993a4',
     }
 
-    cmap = {
+    cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {
         0: (0, 0, 0, 255),
         1: (255, 211, 0, 255),
         2: (255, 37, 37, 255),

torchgeo/datasets/chabud.py

@@ -4,7 +4,8 @@
 """ChaBuD dataset."""
 
 import os
-from collections.abc import Callable
+from collections.abc import Callable, Sequence
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -53,7 +54,7 @@ class ChaBuD(NonGeoDataset):
     .. versionadded:: 0.6
     """
 
-    all_bands = [
+    all_bands = (
         'B01',
         'B02',
         'B03',
@@ -66,10 +67,10 @@ class ChaBuD(NonGeoDataset):
         'B09',
         'B11',
         'B12',
-    ]
+    )
-    rgb_bands = ['B04', 'B03', 'B02']
+    rgb_bands = ('B04', 'B03', 'B02')
-    folds = {'train': [1, 2, 3, 4], 'val': [0]}
+    folds: ClassVar[dict[str, list[int]]] = {'train': [1, 2, 3, 4], 'val': [0]}
-    url = 'https://hf.co/datasets/chabud-team/chabud-ecml-pkdd2023/resolve/de222d434e26379aa3d4f3dd1b2caf502427a8b2/train_eval.hdf5'  # noqa: E501
+    url = 'https://hf.co/datasets/chabud-team/chabud-ecml-pkdd2023/resolve/de222d434e26379aa3d4f3dd1b2caf502427a8b2/train_eval.hdf5'
     filename = 'train_eval.hdf5'
     md5 = '15d78fb825f9a81dad600db828d22c08'
 
@@ -77,7 +78,7 @@ class ChaBuD(NonGeoDataset):
         self,
         root: Path = 'data',
         split: str = 'train',
-        bands: list[str] = all_bands,
+        bands: Sequence[str] = all_bands,
         transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
         download: bool = False,
         checksum: bool = False,

torchgeo/datasets/chesapeake.py

@@ -9,7 +9,7 @@ import pathlib
 import sys
 from abc import ABC, abstractmethod
 from collections.abc import Callable, Iterable, Sequence
-from typing import Any, cast
+from typing import Any, ClassVar, cast
 
 import fiona
 import matplotlib.pyplot as plt
@@ -39,7 +39,7 @@ class Chesapeake(RasterDataset, ABC):
 
     The Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates
     characterization of the landscape and land change for and between discrete time
-    periods. The database was developed by the University of Vermont’s Spatial Analysis
+    periods. The database was developed by the University of Vermont's Spatial Analysis
     Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological
     Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake
     Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate
@@ -83,7 +83,7 @@ class Chesapeake(RasterDataset, ABC):
         """State abbreviation."""
         return self.__class__.__name__[-2:].lower()
 
-    cmap = {
+    cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {
         11: (0, 92, 230, 255),
         12: (0, 92, 230, 255),
         13: (0, 92, 230, 255),
@@ -255,7 +255,7 @@ class Chesapeake(RasterDataset, ABC):
 class ChesapeakeDC(Chesapeake):
     """This subset of the dataset contains data only for Washington, D.C."""
 
-    md5s = {
+    md5s: ClassVar[dict[int, str]] = {
         2013: '9f1df21afbb9d5c0fcf33af7f6750a7f',
         2017: 'c45e4af2950e1c93ecd47b61af296d9b',
     }
@@ -264,7 +264,7 @@ class ChesapeakeDC(Chesapeake):
 class ChesapeakeDE(Chesapeake):
     """This subset of the dataset contains data only for Delaware."""
 
-    md5s = {
+    md5s: ClassVar[dict[int, str]] = {
         2013: '5850d96d897babba85610658aeb5951a',
         2018: 'ee94c8efeae423d898677104117bdebc',
     }
@@ -273,7 +273,7 @@ class ChesapeakeDE(Chesapeake):
 class ChesapeakeMD(Chesapeake):
     """This subset of the dataset contains data only for Maryland."""
 
-    md5s = {
+    md5s: ClassVar[dict[int, str]] = {
         2013: '9c3ca5040668d15284c1bd64b7d6c7a0',
         2018: '0647530edf8bec6e60f82760dcc7db9c',
     }
@@ -282,7 +282,7 @@ class ChesapeakeMD(Chesapeake):
 class ChesapeakeNY(Chesapeake):
     """This subset of the dataset contains data only for New York."""
 
-    md5s = {
+    md5s: ClassVar[dict[int, str]] = {
         2013: '38a29b721610ba661a7f8b6ec71a48b7',
         2017: '4c1b1a50fd9368cd7b8b12c4d80c63f3',
     }
@@ -291,7 +291,7 @@ class ChesapeakeNY(Chesapeake):
 class ChesapeakePA(Chesapeake):
     """This subset of the dataset contains data only for Pennsylvania."""
 
-    md5s = {
+    md5s: ClassVar[dict[int, str]] = {
         2013: '86febd603a120a49ef7d23ef486152a3',
         2017: 'b11d92e4471e8cb887c790d488a338c1',
     }
@@ -300,7 +300,7 @@ class ChesapeakePA(Chesapeake):
 class ChesapeakeVA(Chesapeake):
     """This subset of the dataset contains data only for Virginia."""
 
-    md5s = {
+    md5s: ClassVar[dict[int, str]] = {
         2014: '49c9700c71854eebd00de24d8488eb7c',
         2018: '51731c8b5632978bfd1df869ea10db5b',
     }
@@ -309,7 +309,7 @@ class ChesapeakeVA(Chesapeake):
 class ChesapeakeWV(Chesapeake):
     """This subset of the dataset contains data only for West Virginia."""
 
-    md5s = {
+    md5s: ClassVar[dict[int, str]] = {
         2014: '32fea42fae147bd58a83e3ea6cccfb94',
         2018: '80f25dcba72e39685ab33215c5d97292',
     }
@@ -337,16 +337,16 @@ class ChesapeakeCVPR(GeoDataset):
     * https://doi.org/10.1109/cvpr.2019.01301
     """
 
-    subdatasets = ['base', 'prior_extension']
+    subdatasets = ('base', 'prior_extension')
-    urls = {
+    urls: ClassVar[dict[str, str]] = {
-        'base': 'https://lilablobssc.blob.core.windows.net/lcmcvpr2019/cvpr_chesapeake_landcover.zip',  # noqa: E501
+        'base': 'https://lilablobssc.blob.core.windows.net/lcmcvpr2019/cvpr_chesapeake_landcover.zip',
-        'prior_extension': 'https://zenodo.org/record/5866525/files/cvpr_chesapeake_landcover_prior_extension.zip?download=1',  # noqa: E501
+        'prior_extension': 'https://zenodo.org/record/5866525/files/cvpr_chesapeake_landcover_prior_extension.zip?download=1',
     }
-    filenames = {
+    filenames: ClassVar[dict[str, str]] = {
         'base': 'cvpr_chesapeake_landcover.zip',
         'prior_extension': 'cvpr_chesapeake_landcover_prior_extension.zip',
     }
-    md5s = {
+    md5s: ClassVar[dict[str, str]] = {
         'base': '1225ccbb9590e9396875f221e5031514',
         'prior_extension': '402f41d07823c8faf7ea6960d7c4e17a',
     }
@@ -354,7 +354,7 @@ class ChesapeakeCVPR(GeoDataset):
     crs = CRS.from_epsg(3857)
     res = 1
 
-    lc_cmap = {
+    lc_cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {
         0: (0, 0, 0, 0),
         1: (0, 197, 255, 255),
         2: (38, 115, 0, 255),
@@ -374,7 +374,7 @@ class ChesapeakeCVPR(GeoDataset):
         ]
     )
 
-    valid_layers = [
+    valid_layers = (
         'naip-new',
         'naip-old',
         'landsat-leaf-on',
@@ -383,8 +383,8 @@ class ChesapeakeCVPR(GeoDataset):
         'lc',
         'buildings',
         'prior_from_cooccurrences_101_31_no_osm_no_buildings',
-    ]
+    )
-    states = ['de', 'md', 'va', 'wv', 'pa', 'ny']
+    states = ('de', 'md', 'va', 'wv', 'pa', 'ny')
     splits = (
         [f'{state}-train' for state in states]
         + [f'{state}-val' for state in states]
@@ -392,7 +392,7 @@ class ChesapeakeCVPR(GeoDataset):
     )
 
     # these are used to check the integrity of the dataset
-    _files = [
+    _files = (
         'de_1m_2013_extended-debuffered-test_tiles',
         'de_1m_2013_extended-debuffered-train_tiles',
         'de_1m_2013_extended-debuffered-val_tiles',
@@ -412,18 +412,18 @@ class ChesapeakeCVPR(GeoDataset):
         'wv_1m_2014_extended-debuffered-train_tiles',
         'wv_1m_2014_extended-debuffered-val_tiles',
         'wv_1m_2014_extended-debuffered-val_tiles/m_3708035_ne_17_1_buildings.tif',
-        'wv_1m_2014_extended-debuffered-val_tiles/m_3708035_ne_17_1_landsat-leaf-off.tif',  # noqa: E501
+        'wv_1m_2014_extended-debuffered-val_tiles/m_3708035_ne_17_1_landsat-leaf-off.tif',
-        'wv_1m_2014_extended-debuffered-val_tiles/m_3708035_ne_17_1_landsat-leaf-on.tif',  # noqa: E501
+        'wv_1m_2014_extended-debuffered-val_tiles/m_3708035_ne_17_1_landsat-leaf-on.tif',
         'wv_1m_2014_extended-debuffered-val_tiles/m_3708035_ne_17_1_lc.tif',
         'wv_1m_2014_extended-debuffered-val_tiles/m_3708035_ne_17_1_naip-new.tif',
         'wv_1m_2014_extended-debuffered-val_tiles/m_3708035_ne_17_1_naip-old.tif',
         'wv_1m_2014_extended-debuffered-val_tiles/m_3708035_ne_17_1_nlcd.tif',
-        'wv_1m_2014_extended-debuffered-val_tiles/m_3708035_ne_17_1_prior_from_cooccurrences_101_31_no_osm_no_buildings.tif',  # noqa: E501
+        'wv_1m_2014_extended-debuffered-val_tiles/m_3708035_ne_17_1_prior_from_cooccurrences_101_31_no_osm_no_buildings.tif',
         'spatial_index.geojson',
-    ]
+    )
 
     p_src_crs = pyproj.CRS('epsg:3857')
-    p_transformers = {
+    p_transformers: ClassVar[dict[str, CRS]] = {
         'epsg:26917': pyproj.Transformer.from_crs(
             p_src_crs, pyproj.CRS('epsg:26917'), always_xy=True
         ).transform,
@@ -511,7 +511,7 @@ class ChesapeakeCVPR(GeoDataset):
                             'lc': row['properties']['lc'],
                             'nlcd': row['properties']['nlcd'],
                             'buildings': row['properties']['buildings'],
-                            'prior_from_cooccurrences_101_31_no_osm_no_buildings': prior_fn,  # noqa: E501
+                            'prior_from_cooccurrences_101_31_no_osm_no_buildings': prior_fn,
                         },
                     )
 

torchgeo/datasets/cloud_cover.py

@@ -5,6 +5,7 @@
 
 import os
 from collections.abc import Callable, Sequence
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -55,9 +56,9 @@ class CloudCoverDetection(NonGeoDataset):
     """
 
     url = 'https://radiantearth.blob.core.windows.net/mlhub/ref_cloud_cover_detection_challenge_v1/final'
-    all_bands = ['B02', 'B03', 'B04', 'B08']
+    all_bands = ('B02', 'B03', 'B04', 'B08')
-    rgb_bands = ['B04', 'B03', 'B02']
+    rgb_bands = ('B04', 'B03', 'B02')
-    splits = {'train': 'public', 'test': 'private'}
+    splits: ClassVar[dict[str, str]] = {'train': 'public', 'test': 'private'}
 
     def __init__(
         self,

torchgeo/datasets/cms_mangrove_canopy.py

@@ -42,7 +42,7 @@ class CMSGlobalMangroveCanopy(RasterDataset):
     zipfile = 'CMS_Global_Map_Mangrove_Canopy_1665.zip'
     md5 = '3e7f9f23bf971c25e828b36e6c5496e3'
 
-    all_countries = [
+    all_countries = (
         'AndamanAndNicobar',
         'Angola',
         'Anguilla',
@@ -164,9 +164,9 @@ class CMSGlobalMangroveCanopy(RasterDataset):
         'VirginIslandsUs',
         'WallisAndFutuna',
         'Yemen',
-    ]
+    )
 
-    measurements = ['agb', 'hba95', 'hmax95']
+    measurements = ('agb', 'hba95', 'hmax95')
 
     def __init__(
         self,

torchgeo/datasets/cowc.py

@@ -50,12 +50,12 @@ class COWC(NonGeoDataset, abc.ABC):
 
     @property
     @abc.abstractmethod
-    def filenames(self) -> list[str]:
+    def filenames(self) -> tuple[str, ...]:
         """List of files to download."""
 
     @property
     @abc.abstractmethod
-    def md5s(self) -> list[str]:
+    def md5s(self) -> tuple[str, ...]:
         """List of MD5 checksums of files to download."""
 
     @property
@@ -239,7 +239,7 @@ class COWCCounting(COWC):
     base_url = (
         'https://gdo152.llnl.gov/cowc/download/cowc/datasets/patch_sets/counting/'
     )
-    filenames = [
+    filenames = (
         'COWC_train_list_64_class.txt.bz2',
         'COWC_test_list_64_class.txt.bz2',
         'COWC_Counting_Toronto_ISPRS.tbz',
@@ -248,8 +248,8 @@ class COWCCounting(COWC):
         'COWC_Counting_Vaihingen_ISPRS.tbz',
         'COWC_Counting_Columbus_CSUAV_AFRL.tbz',
         'COWC_Counting_Utah_AGRC.tbz',
-    ]
+    )
-    md5s = [
+    md5s = (
         '187543d20fa6d591b8da51136e8ef8fb',
         '930cfd6e160a7b36db03146282178807',
         'bc2613196dfa93e66d324ae43e7c1fdb',
@@ -258,7 +258,7 @@ class COWCCounting(COWC):
         '4009c1e420566390746f5b4db02afdb9',
         'daf8033c4e8ceebbf2c3cac3fabb8b10',
         '777ec107ed2a3d54597a739ce74f95ad',
-    ]
+    )
     filename = 'COWC_{}_list_64_class.txt'
 
 
@@ -268,7 +268,7 @@ class COWCDetection(COWC):
     base_url = (
         'https://gdo152.llnl.gov/cowc/download/cowc/datasets/patch_sets/detection/'
     )
-    filenames = [
+    filenames = (
         'COWC_train_list_detection.txt.bz2',
         'COWC_test_list_detection.txt.bz2',
         'COWC_Detection_Toronto_ISPRS.tbz',
@@ -277,8 +277,8 @@ class COWCDetection(COWC):
         'COWC_Detection_Vaihingen_ISPRS.tbz',
         'COWC_Detection_Columbus_CSUAV_AFRL.tbz',
         'COWC_Detection_Utah_AGRC.tbz',
-    ]
+    )
-    md5s = [
+    md5s = (
         'c954a5a3dac08c220b10cfbeec83893c',
         'c6c2d0a78f12a2ad88b286b724a57c1a',
         '11af24f43b198b0f13c8e94814008a48',
@@ -287,7 +287,7 @@ class COWCDetection(COWC):
         '23945d5b22455450a938382ccc2a8b27',
         'f40522dc97bea41b10117d4a5b946a6f',
         '195da7c9443a939a468c9f232fd86ee3',
-    ]
+    )
     filename = 'COWC_{}_list_detection.txt'
 
 

torchgeo/datasets/cropharvest.py

@@ -7,6 +7,7 @@ import glob
 import json
 import os
 from collections.abc import Callable
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -55,7 +56,7 @@ class CropHarvest(NonGeoDataset):
     """
 
     # https://github.com/nasaharvest/cropharvest/blob/main/cropharvest/bands.py
-    all_bands = [
+    all_bands = (
         'VV',
         'VH',
         'B2',
@@ -74,12 +75,12 @@ class CropHarvest(NonGeoDataset):
         'elevation',
         'slope',
         'NDVI',
-    ]
+    )
-    rgb_bands = ['B4', 'B3', 'B2']
+    rgb_bands = ('B4', 'B3', 'B2')
 
     features_url = 'https://zenodo.org/records/7257688/files/features.tar.gz?download=1'
     labels_url = 'https://zenodo.org/records/7257688/files/labels.geojson?download=1'
-    file_dict = {
+    file_dict: ClassVar[dict[str, dict[str, str]]] = {
         'features': {
             'url': features_url,
             'filename': 'features.tar.gz',

torchgeo/datasets/cv4a_kenya_crop_type.py

@@ -65,8 +65,8 @@ class CV4AKenyaCropType(NonGeoDataset):
     """
 
     url = 'https://radiantearth.blob.core.windows.net/mlhub/kenya-crop-challenge'
-    tiles = list(map(str, range(4)))
+    tiles = tuple(map(str, range(4)))
-    dates = [
+    dates = (
         '20190606',
         '20190701',
         '20190706',
@@ -80,7 +80,7 @@ class CV4AKenyaCropType(NonGeoDataset):
         '20190924',
         '20191004',
         '20191103',
-    ]
+    )
     all_bands = (
         'B01',
         'B02',
@@ -96,7 +96,7 @@ class CV4AKenyaCropType(NonGeoDataset):
         'B12',
         'CLD',
     )
-    rgb_bands = ['B04', 'B03', 'B02']
+    rgb_bands = ('B04', 'B03', 'B02')
 
     # Same for all tiles
     tile_height = 3035
@@ -141,11 +141,13 @@ class CV4AKenyaCropType(NonGeoDataset):
         # Calculate the indices that we will use over all tiles
         self.chips_metadata = []
         for tile_index in range(len(self.tiles)):
-            for y in list(range(0, self.tile_height - self.chip_size, stride)) + [
+            for y in [
-                self.tile_height - self.chip_size
+                *list(range(0, self.tile_height - self.chip_size, stride)),
+                self.tile_height - self.chip_size,
             ]:
-                for x in list(range(0, self.tile_width - self.chip_size, stride)) + [
+                for x in [
-                    self.tile_width - self.chip_size
+                    *list(range(0, self.tile_width - self.chip_size, stride)),
+                    self.tile_width - self.chip_size,
                 ]:
                     self.chips_metadata.append((tile_index, y, x))
 

torchgeo/datasets/deepglobelandcover.py

@@ -74,13 +74,13 @@ class DeepGlobeLandCover(NonGeoDataset):
           $ unzip deepglobe2018-landcover-segmentation-traindataset.zip
 
     .. versionadded:: 0.3
-    """  # noqa: E501
+    """
 
     filename = 'data.zip'
     data_root = 'data'
     md5 = 'f32684b0b2bf6f8d604cd359a399c061'
-    splits = ['train', 'test']
+    splits = ('train', 'test')
-    classes = [
+    classes = (
         'Urban land',
         'Agriculture land',
         'Rangeland',
@@ -88,8 +88,8 @@ class DeepGlobeLandCover(NonGeoDataset):
         'Water',
         'Barren land',
         'Unknown',
-    ]
+    )
-    colormap = [
+    colormap = (
         (0, 255, 255),
         (255, 255, 0),
         (255, 0, 255),
@@ -97,7 +97,7 @@ class DeepGlobeLandCover(NonGeoDataset):
         (0, 0, 255),
         (255, 255, 255),
         (0, 0, 0),
-    ]
+    )
 
     def __init__(
         self,
@@ -246,12 +246,15 @@ class DeepGlobeLandCover(NonGeoDataset):
         """
         ncols = 1
         image1 = draw_semantic_segmentation_masks(
-            sample['image'], sample['mask'], alpha=alpha, colors=self.colormap
+            sample['image'], sample['mask'], alpha=alpha, colors=list(self.colormap)
         )
         if 'prediction' in sample:
             ncols += 1
             image2 = draw_semantic_segmentation_masks(
-                sample['image'], sample['prediction'], alpha=alpha, colors=self.colormap
+                sample['image'],
+                sample['prediction'],
+                alpha=alpha,
+                colors=list(self.colormap),
             )
 
         fig, axs = plt.subplots(ncols=ncols, figsize=(ncols * 10, 10))

torchgeo/datasets/dfc2022.py

@@ -6,6 +6,7 @@
 import glob
 import os
 from collections.abc import Callable, Sequence
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -75,9 +76,9 @@ class DFC2022(NonGeoDataset):
     * https://doi.org/10.1007/s10994-020-05943-y
 
     .. versionadded:: 0.3
-    """  # noqa: E501
+    """
 
-    classes = [
+    classes = (
         'No information',
         'Urban fabric',
         'Industrial, commercial, public, military, private and transport units',
@@ -94,8 +95,8 @@ class DFC2022(NonGeoDataset):
         'Wetlands',
         'Water',
         'Clouds and Shadows',
-    ]
+    )
-    colormap = [
+    colormap = (
         '#231F20',
         '#DB5F57',
         '#DB9757',
@@ -112,8 +113,8 @@ class DFC2022(NonGeoDataset):
         '#579BDB',
         '#0062FF',
         '#231F20',
-    ]
+    )
-    metadata = {
+    metadata: ClassVar[dict[str, dict[str, str]]] = {
         'train': {
             'filename': 'labeled_train.zip',
             'md5': '2e87d6a218e466dd0566797d7298c7a9',

torchgeo/datasets/enviroatlas.py

@@ -6,7 +6,7 @@
 import os
 import sys
 from collections.abc import Callable, Sequence
-from typing import Any, cast
+from typing import Any, ClassVar, cast
 
 import fiona
 import matplotlib.pyplot as plt
@@ -54,9 +54,9 @@ class EnviroAtlas(GeoDataset):
     crs = CRS.from_epsg(3857)
     res = 1
 
-    valid_prior_layers = ['prior', 'prior_no_osm_no_buildings']
+    valid_prior_layers = ('prior', 'prior_no_osm_no_buildings')
 
-    valid_layers = [
+    valid_layers = (
         'naip',
         'nlcd',
         'roads',
@@ -65,14 +65,15 @@ class EnviroAtlas(GeoDataset):
         'waterbodies',
         'buildings',
         'lc',
-    ] + valid_prior_layers
+        *valid_prior_layers,
+    )
 
-    cities = [
+    cities = (
         'pittsburgh_pa-2010_1m',
         'durham_nc-2012_1m',
         'austin_tx-2012_1m',
         'phoenix_az-2010_1m',
-    ]
+    )
     splits = (
         [f'{state}-train' for state in cities[:1]]
         + [f'{state}-val' for state in cities[:1]]
@@ -81,7 +82,7 @@ class EnviroAtlas(GeoDataset):
     )
 
     # these are used to check the integrity of the dataset
-    _files = [
+    _files = (
         'austin_tx-2012_1m-test_tiles-debuffered',
         'austin_tx-2012_1m-val5_tiles-debuffered',
         'durham_nc-2012_1m-test_tiles-debuffered',
@@ -100,13 +101,13 @@ class EnviroAtlas(GeoDataset):
         'austin_tx-2012_1m-test_tiles-debuffered/3009726_sw_d_water.tif',
         'austin_tx-2012_1m-test_tiles-debuffered/3009726_sw_e_buildings.tif',
         'austin_tx-2012_1m-test_tiles-debuffered/3009726_sw_h_highres_labels.tif',
-        'austin_tx-2012_1m-test_tiles-debuffered/3009726_sw_prior_from_cooccurrences_101_31.tif',  # noqa: E501
+        'austin_tx-2012_1m-test_tiles-debuffered/3009726_sw_prior_from_cooccurrences_101_31.tif',
-        'austin_tx-2012_1m-test_tiles-debuffered/3009726_sw_prior_from_cooccurrences_101_31_no_osm_no_buildings.tif',  # noqa: E501
+        'austin_tx-2012_1m-test_tiles-debuffered/3009726_sw_prior_from_cooccurrences_101_31_no_osm_no_buildings.tif',
         'spatial_index.geojson',
-    ]
+    )
 
     p_src_crs = pyproj.CRS('epsg:3857')
-    p_transformers = {
+    p_transformers: ClassVar[dict[str, CRS]] = {
         'epsg:26917': pyproj.Transformer.from_crs(
             p_src_crs, pyproj.CRS('epsg:26917'), always_xy=True
         ).transform,
@@ -222,7 +223,7 @@ class EnviroAtlas(GeoDataset):
         dtype=np.uint8,
     )
 
-    highres_classes = [
+    highres_classes = (
         'Unclassified',
         'Water',
         'Impervious Surface',
@@ -234,7 +235,7 @@ class EnviroAtlas(GeoDataset):
         'Orchards',
         'Woody Wetlands',
         'Emergent Wetlands',
-    ]
+    )
     highres_cmap = ListedColormap(
         [
             [1.00000000, 1.00000000, 1.00000000],

torchgeo/datasets/etci2021.py

@@ -6,6 +6,7 @@
 import glob
 import os
 from collections.abc import Callable
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -56,9 +57,9 @@ class ETCI2021(NonGeoDataset):
         the ETCI competition.
     """
 
-    bands = ['VV', 'VH']
+    bands = ('VV', 'VH')
-    masks = ['flood', 'water_body']
+    masks = ('flood', 'water_body')
-    metadata = {
+    metadata: ClassVar[dict[str, dict[str, str]]] = {
         'train': {
             'filename': 'train.zip',
             'md5': '1e95792fe0f6e3c9000abdeab2a8ab0f',

torchgeo/datasets/eudem.py

@@ -7,7 +7,7 @@ import glob
 import os
 import pathlib
 from collections.abc import Callable, Iterable
-from typing import Any
+from typing import Any, ClassVar
 
 import matplotlib.pyplot as plt
 from matplotlib.figure import Figure
@@ -53,7 +53,7 @@ class EUDEM(RasterDataset):
     zipfile_glob = 'eu_dem_v11_*[A-Z0-9].zip'
     filename_regex = '(?P<name>[eudem_v11]{10})_(?P<id>[A-Z0-9]{6})'
 
-    md5s = {
+    md5s: ClassVar[dict[str, str]] = {
         'eu_dem_v11_E00N20.zip': '96edc7e11bc299b994e848050d6be591',
         'eu_dem_v11_E10N00.zip': 'e14be147ac83eddf655f4833d55c1571',
         'eu_dem_v11_E10N10.zip': '2eb5187e4d827245b33768404529c709',

torchgeo/datasets/eurocrops.py

@@ -61,7 +61,7 @@ class EuroCrops(VectorDataset):
     date_format = '%Y'
 
     # Filename and md5 of files in this dataset on zenodo.
-    zenodo_files = [
+    zenodo_files: tuple[tuple[str, str], ...] = (
         ('AT_2021.zip', '490241df2e3d62812e572049fc0c36c5'),
         ('BE_VLG_2021.zip', 'ac4b9e12ad39b1cba47fdff1a786c2d7'),
         ('DE_LS_2021.zip', '6d94e663a3ff7988b32cb36ea24a724f'),
@@ -81,7 +81,7 @@ class EuroCrops(VectorDataset):
         # Year is unknown for Romania portion (ny = no year).
         # We skip since it is inconsistent with the rest of the data.
         # ("RO_ny.zip", "648e1504097765b4b7f825decc838882"),
-    ]
+    )
 
     def __init__(
         self,

torchgeo/datasets/eurosat.py

@@ -5,7 +5,7 @@
 
 import os
 from collections.abc import Callable, Sequence
-from typing import cast
+from typing import ClassVar, cast
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -54,7 +54,7 @@ class EuroSAT(NonGeoClassificationDataset):
     * https://ieeexplore.ieee.org/document/8519248
     """
 
-    url = 'https://hf.co/datasets/torchgeo/eurosat/resolve/06fd1b090bceecc0ce724cd21578ba7a6664fe8d/EuroSATallBands.zip'  # noqa: E501
+    url = 'https://hf.co/datasets/torchgeo/eurosat/resolve/06fd1b090bceecc0ce724cd21578ba7a6664fe8d/EuroSATallBands.zip'
     filename = 'EuroSATallBands.zip'
     md5 = '5ac12b3b2557aa56e1826e981e8e200e'
 
@@ -63,13 +63,13 @@ class EuroSAT(NonGeoClassificationDataset):
         'ds', 'images', 'remote_sensing', 'otherDatasets', 'sentinel_2', 'tif'
     )
 
-    splits = ['train', 'val', 'test']
+    splits = ('train', 'val', 'test')
-    split_urls = {
+    split_urls: ClassVar[dict[str, str]] = {
-        'train': 'https://storage.googleapis.com/remote_sensing_representations/eurosat-train.txt',  # noqa: E501
+        'train': 'https://storage.googleapis.com/remote_sensing_representations/eurosat-train.txt',
-        'val': 'https://storage.googleapis.com/remote_sensing_representations/eurosat-val.txt',  # noqa: E501
+        'val': 'https://storage.googleapis.com/remote_sensing_representations/eurosat-val.txt',
-        'test': 'https://storage.googleapis.com/remote_sensing_representations/eurosat-test.txt',  # noqa: E501
+        'test': 'https://storage.googleapis.com/remote_sensing_representations/eurosat-test.txt',
     }
-    split_md5s = {
+    split_md5s: ClassVar[dict[str, str]] = {
         'train': '908f142e73d6acdf3f482c5e80d851b1',
         'val': '95de90f2aa998f70a3b2416bfe0687b4',
         'test': '7ae5ab94471417b6e315763121e67c5f',
@@ -93,7 +93,10 @@ class EuroSAT(NonGeoClassificationDataset):
 
     rgb_bands = ('B04', 'B03', 'B02')
 
-    BAND_SETS = {'all': all_band_names, 'rgb': rgb_bands}
+    BAND_SETS: ClassVar[dict[str, tuple[str, ...]]] = {
+        'all': all_band_names,
+        'rgb': rgb_bands,
+    }
 
     def __init__(
         self,
@@ -302,12 +305,12 @@ class EuroSATSpatial(EuroSAT):
     .. versionadded:: 0.6
     """
 
-    split_urls = {
+    split_urls: ClassVar[dict[str, str]] = {
         'train': 'https://hf.co/datasets/torchgeo/eurosat/resolve/1c11c73a87b40b0485d103231a97829991b8e22f/eurosat-spatial-train.txt',
         'val': 'https://hf.co/datasets/torchgeo/eurosat/resolve/1c11c73a87b40b0485d103231a97829991b8e22f/eurosat-spatial-val.txt',
         'test': 'https://hf.co/datasets/torchgeo/eurosat/resolve/1c11c73a87b40b0485d103231a97829991b8e22f/eurosat-spatial-test.txt',
     }
-    split_md5s = {
+    split_md5s: ClassVar[dict[str, str]] = {
         'train': '7be3254be39f23ce4d4d144290c93292',
         'val': 'acf392290050bb3df790dc8fc0ebf193',
         'test': '5ec1733f9c16116bf0aa2d921fc613ef',
@@ -325,16 +328,16 @@ class EuroSAT100(EuroSAT):
     .. versionadded:: 0.5
     """
 
-    url = 'https://hf.co/datasets/torchgeo/eurosat/resolve/06fd1b090bceecc0ce724cd21578ba7a6664fe8d/EuroSAT100.zip'  # noqa: E501
+    url = 'https://hf.co/datasets/torchgeo/eurosat/resolve/06fd1b090bceecc0ce724cd21578ba7a6664fe8d/EuroSAT100.zip'
     filename = 'EuroSAT100.zip'
     md5 = 'c21c649ba747e86eda813407ef17d596'
 
-    split_urls = {
+    split_urls: ClassVar[dict[str, str]] = {
-        'train': 'https://hf.co/datasets/torchgeo/eurosat/resolve/06fd1b090bceecc0ce724cd21578ba7a6664fe8d/eurosat-train.txt',  # noqa: E501
+        'train': 'https://hf.co/datasets/torchgeo/eurosat/resolve/06fd1b090bceecc0ce724cd21578ba7a6664fe8d/eurosat-train.txt',
-        'val': 'https://hf.co/datasets/torchgeo/eurosat/resolve/06fd1b090bceecc0ce724cd21578ba7a6664fe8d/eurosat-val.txt',  # noqa: E501
+        'val': 'https://hf.co/datasets/torchgeo/eurosat/resolve/06fd1b090bceecc0ce724cd21578ba7a6664fe8d/eurosat-val.txt',
-        'test': 'https://hf.co/datasets/torchgeo/eurosat/resolve/06fd1b090bceecc0ce724cd21578ba7a6664fe8d/eurosat-test.txt',  # noqa: E501
+        'test': 'https://hf.co/datasets/torchgeo/eurosat/resolve/06fd1b090bceecc0ce724cd21578ba7a6664fe8d/eurosat-test.txt',
     }
-    split_md5s = {
+    split_md5s: ClassVar[dict[str, str]] = {
         'train': '033d0c23e3a75e3fa79618b0e35fe1c7',
         'val': '3e3f8b3c344182b8d126c4cc88f3f215',
         'test': 'f908f151b950f270ad18e61153579794',

torchgeo/datasets/fair1m.py

@@ -6,7 +6,7 @@
 import glob
 import os
 from collections.abc import Callable
-from typing import Any, cast
+from typing import Any, ClassVar, cast
 from xml.etree.ElementTree import Element, parse
 
 import matplotlib.patches as patches
@@ -119,7 +119,7 @@ class FAIR1M(NonGeoDataset):
     .. versionadded:: 0.2
     """
 
-    classes = {
+    classes: ClassVar[dict[str, dict[str, Any]]] = {
         'Passenger Ship': {'id': 0, 'category': 'Ship'},
         'Motorboat': {'id': 1, 'category': 'Ship'},
         'Fishing Boat': {'id': 2, 'category': 'Ship'},
@@ -159,12 +159,12 @@ class FAIR1M(NonGeoDataset):
         'Bridge': {'id': 36, 'category': 'Road'},
     }
 
-    filename_glob = {
+    filename_glob: ClassVar[dict[str, str]] = {
         'train': os.path.join('train', '**', 'images', '*.tif'),
         'val': os.path.join('validation', 'images', '*.tif'),
         'test': os.path.join('test', 'images', '*.tif'),
     }
-    directories = {
+    directories: ClassVar[dict[str, tuple[str, ...]]] = {
         'train': (
             os.path.join('train', 'part1', 'images'),
             os.path.join('train', 'part1', 'labelXml'),
@@ -175,9 +175,9 @@ class FAIR1M(NonGeoDataset):
             os.path.join('validation', 'images'),
             os.path.join('validation', 'labelXml'),
         ),
-        'test': (os.path.join('test', 'images')),
+        'test': (os.path.join('test', 'images'),),
     }
-    paths = {
+    paths: ClassVar[dict[str, tuple[str, ...]]] = {
         'train': (
             os.path.join('train', 'part1', 'images.zip'),
             os.path.join('train', 'part1', 'labelXml.zip'),
@@ -194,7 +194,7 @@ class FAIR1M(NonGeoDataset):
             os.path.join('test', 'images2.zip'),
         ),
     }
-    urls = {
+    urls: ClassVar[dict[str, tuple[str, ...]]] = {
         'train': (
             'https://drive.google.com/file/d/1LWT_ybL-s88Lzg9A9wHpj0h2rJHrqrVf',
             'https://drive.google.com/file/d/1CnOuS8oX6T9JMqQnfFsbmf7U38G6Vc8u',
@@ -211,7 +211,7 @@ class FAIR1M(NonGeoDataset):
             'https://drive.google.com/file/d/1oUc25FVf8Zcp4pzJ31A1j1sOLNHu63P0',
         ),
     }
-    md5s = {
+    md5s: ClassVar[dict[str, tuple[str, ...]]] = {
         'train': (
             'a460fe6b1b5b276bf856ce9ac72d6568',
             '80f833ff355f91445c92a0c0c1fa7414',

torchgeo/datasets/fire_risk.py

@@ -55,8 +55,8 @@ class FireRisk(NonGeoClassificationDataset):
     md5 = 'a77b9a100d51167992ae8c51d26198a6'
     filename = 'FireRisk.zip'
     directory = 'FireRisk'
-    splits = ['train', 'val']
+    splits = ('train', 'val')
-    classes = [
+    classes = (
         'High',
         'Low',
         'Moderate',
@@ -64,7 +64,7 @@ class FireRisk(NonGeoClassificationDataset):
         'Very_High',
         'Very_Low',
         'Water',
-    ]
+    )
 
     def __init__(
         self,

torchgeo/datasets/forestdamage.py

@@ -96,11 +96,8 @@ class ForestDamage(NonGeoDataset):
     .. versionadded:: 0.3
     """
 
-    classes = ['other', 'H', 'LD', 'HD']
+    classes = ('other', 'H', 'LD', 'HD')
-    url = (
+    url = 'https://lilablobssc.blob.core.windows.net/larch-casebearer/Data_Set_Larch_Casebearer.zip'
-        'https://lilablobssc.blob.core.windows.net/larch-casebearer/'
-        'Data_Set_Larch_Casebearer.zip'
-    )
     data_dir = 'Data_Set_Larch_Casebearer'
     md5 = '907815bcc739bff89496fac8f8ce63d7'
 

torchgeo/datasets/geo.py

@@ -13,7 +13,7 @@ import re
 import sys
 import warnings
 from collections.abc import Callable, Iterable, Sequence
-from typing import Any, cast
+from typing import Any, ClassVar, cast
 
 import fiona
 import fiona.transform
@@ -370,13 +370,13 @@ class RasterDataset(GeoDataset):
     separate_files = False
 
     #: Names of all available bands in the dataset
-    all_bands: list[str] = []
+    all_bands: tuple[str, ...] = ()
 
     #: Names of RGB bands in the dataset, used for plotting
-    rgb_bands: list[str] = []
+    rgb_bands: tuple[str, ...] = ()
 
     #: Color map for the dataset, used for plotting
-    cmap: dict[int, tuple[int, int, int, int]] = {}
+    cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {}
 
     @property
     def dtype(self) -> torch.dtype:
@@ -458,7 +458,7 @@ class RasterDataset(GeoDataset):
                         # See if file has a color map
                         if len(self.cmap) == 0:
                             try:
-                                self.cmap = src.colormap(1)
+                                self.cmap = src.colormap(1)  # type: ignore[misc]
                             except ValueError:
                                 pass
 

torchgeo/datasets/gid15.py

@@ -66,8 +66,8 @@ class GID15(NonGeoDataset):
     md5 = '615682bf659c3ed981826c6122c10c83'
     filename = 'gid-15.zip'
     directory = 'GID'
-    splits = ['train', 'val', 'test']
+    splits = ('train', 'val', 'test')
-    classes = [
+    classes = (
         'background',
         'industrial_land',
         'urban_residential',
@@ -84,7 +84,7 @@ class GID15(NonGeoDataset):
         'river',
         'lake',
         'pond',
-    ]
+    )
 
     def __init__(
         self,

torchgeo/datasets/globbiomass.py

@@ -7,7 +7,7 @@ import glob
 import os
 import pathlib
 from collections.abc import Callable, Iterable
-from typing import Any, cast
+from typing import Any, ClassVar, cast
 
 import matplotlib.pyplot as plt
 import torch
@@ -73,9 +73,9 @@ class GlobBiomass(RasterDataset):
     is_image = False
     dtype = torch.float32  # pixelwise regression
 
-    measurements = ['agb', 'gsv']
+    measurements = ('agb', 'gsv')
 
-    md5s = {
+    md5s: ClassVar[dict[str, str]] = {
         'N00E020_agb.zip': 'bd83a3a4c143885d1962bde549413be6',
         'N00E020_gsv.zip': 'da5ddb88e369df2d781a0c6be008ae79',
         'N00E060_agb.zip': '85eaca95b939086cc528e396b75bd097',

torchgeo/datasets/idtrees.py

@@ -6,7 +6,7 @@
 import glob
 import os
 from collections.abc import Callable
-from typing import Any, cast, overload
+from typing import Any, ClassVar, cast, overload
 
 import fiona
 import matplotlib.pyplot as plt
@@ -100,7 +100,7 @@ class IDTReeS(NonGeoDataset):
     .. versionadded:: 0.2
     """
 
-    classes = {
+    classes: ClassVar[dict[str, str]] = {
         'ACPE': 'Acer pensylvanicum L.',
         'ACRU': 'Acer rubrum L.',
         'ACSA3': 'Acer saccharum Marshall',
@@ -135,19 +135,22 @@ class IDTReeS(NonGeoDataset):
         'ROPS': 'Robinia pseudoacacia L.',
         'TSCA': 'Tsuga canadensis (L.) Carriere',
     }
-    metadata = {
+    metadata: ClassVar[dict[str, dict[str, str]]] = {
         'train': {
-            'url': 'https://zenodo.org/record/3934932/files/IDTREES_competition_train_v2.zip?download=1',  # noqa: E501
+            'url': 'https://zenodo.org/record/3934932/files/IDTREES_competition_train_v2.zip?download=1',
             'md5': '5ddfa76240b4bb6b4a7861d1d31c299c',
             'filename': 'IDTREES_competition_train_v2.zip',
         },
         'test': {
-            'url': 'https://zenodo.org/record/3934932/files/IDTREES_competition_test_v2.zip?download=1',  # noqa: E501
+            'url': 'https://zenodo.org/record/3934932/files/IDTREES_competition_test_v2.zip?download=1',
             'md5': 'b108931c84a70f2a38a8234290131c9b',
             'filename': 'IDTREES_competition_test_v2.zip',
         },
     }
-    directories = {'train': ['train'], 'test': ['task1', 'task2']}
+    directories: ClassVar[dict[str, list[str]]] = {
+        'train': ['train'],
+        'test': ['task1', 'task2'],
+    }
     image_size = (200, 200)
 
     def __init__(

torchgeo/datasets/iobench.py

@@ -6,7 +6,7 @@
 import glob
 import os
 from collections.abc import Callable, Sequence
-from typing import Any
+from typing import Any, ClassVar
 
 import matplotlib.pyplot as plt
 from matplotlib.figure import Figure
@@ -40,9 +40,9 @@ class IOBench(IntersectionDataset):
     .. versionadded:: 0.6
     """
 
-    url = 'https://hf.co/datasets/torchgeo/io/resolve/c9d9d268cf0b61335941bdc2b6963bf16fc3a6cf/{}.tar.gz'  # noqa: E501
+    url = 'https://hf.co/datasets/torchgeo/io/resolve/c9d9d268cf0b61335941bdc2b6963bf16fc3a6cf/{}.tar.gz'
 
-    md5s = {
+    md5s: ClassVar[dict[str, str]] = {
         'original': 'e3a908a0fd1c05c1af2f4c65724d59b3',
         'raw': 'e9603990441007ce7bba73bb8ba7d217',
         'preprocessed': '9801f1240b238cb17525c865e413d1fd',
@@ -54,7 +54,7 @@ class IOBench(IntersectionDataset):
         split: str = 'preprocessed',
         crs: CRS | None = None,
         res: float | None = None,
-        bands: Sequence[str] | None = Landsat9.default_bands + ['SR_QA_AEROSOL'],
+        bands: Sequence[str] | None = [*Landsat9.default_bands, 'SR_QA_AEROSOL'],
         classes: list[int] = [0],
         transforms: Callable[[dict[str, Any]], dict[str, Any]] | None = None,
         cache: bool = True,

torchgeo/datasets/l7irish.py

@@ -8,7 +8,7 @@ import os
 import pathlib
 import re
 from collections.abc import Callable, Iterable, Sequence
-from typing import Any, cast
+from typing import Any, ClassVar, cast
 
 import matplotlib.pyplot as plt
 import torch
@@ -43,8 +43,8 @@ class L7IrishImage(RasterDataset):
     """
     date_format = '%Y%m%d'
     is_image = True
-    rgb_bands = ['B30', 'B20', 'B10']
+    rgb_bands = ('B30', 'B20', 'B10')
-    all_bands = ['B10', 'B20', 'B30', 'B40', 'B50', 'B61', 'B62', 'B70', 'B80']
+    all_bands = ('B10', 'B20', 'B30', 'B40', 'B50', 'B61', 'B62', 'B70', 'B80')
 
 
 class L7IrishMask(RasterDataset):
@@ -59,7 +59,7 @@ class L7IrishMask(RasterDataset):
         _newmask2015\.TIF$
     """
     is_image = False
-    classes = ['Fill', 'Cloud Shadow', 'Clear', 'Thin Cloud', 'Cloud']
+    classes = ('Fill', 'Cloud Shadow', 'Clear', 'Thin Cloud', 'Cloud')
     ordinal_map = torch.zeros(256, dtype=torch.long)
     ordinal_map[64] = 1
     ordinal_map[128] = 2
@@ -158,11 +158,11 @@ class L7Irish(IntersectionDataset):
     * https://www.sciencebase.gov/catalog/item/573ccf18e4b0dae0d5e4b109
 
     .. versionadded:: 0.5
-    """  # noqa: E501
+    """
 
-    url = 'https://hf.co/datasets/torchgeo/l7irish/resolve/6807e0b22eca7f9a8a3903ea673b31a115837464/{}.tar.gz'  # noqa: E501
+    url = 'https://hf.co/datasets/torchgeo/l7irish/resolve/6807e0b22eca7f9a8a3903ea673b31a115837464/{}.tar.gz'
 
-    md5s = {
+    md5s: ClassVar[dict[str, str]] = {
         'austral': '0a34770b992a62abeb88819feb192436',
         'boreal': 'b7cfdd689a3c2fd2a8d572e1c10ed082',
         'mid_latitude_north': 'c40abe5ad2487f8ab021cfb954982faa',

torchgeo/datasets/l8biome.py

@@ -7,7 +7,7 @@ import glob
 import os
 import pathlib
 from collections.abc import Callable, Iterable, Sequence
-from typing import Any
+from typing import Any, ClassVar
 
 import matplotlib.pyplot as plt
 import torch
@@ -36,8 +36,8 @@ class L8BiomeImage(RasterDataset):
     """
     date_format = '%Y%j'
     is_image = True
-    rgb_bands = ['B4', 'B3', 'B2']
+    rgb_bands = ('B4', 'B3', 'B2')
-    all_bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9', 'B10', 'B11']
+    all_bands = ('B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9', 'B10', 'B11')
 
 
 class L8BiomeMask(RasterDataset):
@@ -57,7 +57,7 @@ class L8BiomeMask(RasterDataset):
     """
     date_format = '%Y%j'
     is_image = False
-    classes = ['Fill', 'Cloud Shadow', 'Clear', 'Thin Cloud', 'Cloud']
+    classes = ('Fill', 'Cloud Shadow', 'Clear', 'Thin Cloud', 'Cloud')
     ordinal_map = torch.zeros(256, dtype=torch.long)
     ordinal_map[64] = 1
     ordinal_map[128] = 2
@@ -116,11 +116,11 @@ class L8Biome(IntersectionDataset):
     * https://doi.org/10.1016/j.rse.2017.03.026
 
     .. versionadded:: 0.5
-    """  # noqa: E501
+    """
 
-    url = 'https://hf.co/datasets/torchgeo/l8biome/resolve/f76df19accce34d2acc1878d88b9491bc81f94c8/{}.tar.gz'  # noqa: E501
+    url = 'https://hf.co/datasets/torchgeo/l8biome/resolve/f76df19accce34d2acc1878d88b9491bc81f94c8/{}.tar.gz'
 
-    md5s = {
+    md5s: ClassVar[dict[str, str]] = {
         'barren': '0eb691822d03dabd4f5ea8aadd0b41c3',
         'forest': '4a5645596f6bb8cea44677f746ec676e',
         'grass_crops': 'a69ed5d6cb227c5783f026b9303cdd3c',

torchgeo/datasets/landcoverai.py

@@ -9,7 +9,7 @@ import hashlib
 import os
 from collections.abc import Callable
 from functools import lru_cache
-from typing import Any, cast
+from typing import Any, ClassVar, cast
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -64,8 +64,8 @@ class LandCoverAIBase(Dataset[dict[str, Any]], abc.ABC):
     url = 'https://landcover.ai.linuxpolska.com/download/landcover.ai.v1.zip'
     filename = 'landcover.ai.v1.zip'
     md5 = '3268c89070e8734b4e91d531c0617e03'
-    classes = ['Background', 'Building', 'Woodland', 'Water', 'Road']
+    classes = ('Background', 'Building', 'Woodland', 'Water', 'Road')
-    cmap = {
+    cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {
         0: (0, 0, 0, 0),
         1: (97, 74, 74, 255),
         2: (38, 115, 0, 255),

torchgeo/datasets/landsat.py

@@ -33,7 +33,7 @@ class Landsat(RasterDataset, abc.ABC):
     * `Surface Temperature <https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature>`_
     * `Surface Reflectance <https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance>`_
     * `U.S. Analysis Ready Data <https://www.usgs.gov/landsat-missions/landsat-collection-2-us-analysis-ready-data>`_
-    """  # noqa: E501
+    """
 
     # https://www.usgs.gov/landsat-missions/landsat-collection-2
     filename_regex = r"""
@@ -55,7 +55,7 @@ class Landsat(RasterDataset, abc.ABC):
 
     @property
     @abc.abstractmethod
-    def default_bands(self) -> list[str]:
+    def default_bands(self) -> tuple[str, ...]:
         """Bands to load by default."""
 
     def __init__(
@@ -145,8 +145,8 @@ class Landsat1(Landsat):
 
     filename_glob = 'LM01_*_{}.*'
 
-    default_bands = ['B4', 'B5', 'B6', 'B7']
+    default_bands = ('B4', 'B5', 'B6', 'B7')
-    rgb_bands = ['B6', 'B5', 'B4']
+    rgb_bands = ('B6', 'B5', 'B4')
 
 
 class Landsat2(Landsat1):
@@ -166,8 +166,8 @@ class Landsat4MSS(Landsat):
 
     filename_glob = 'LM04_*_{}.*'
 
-    default_bands = ['B1', 'B2', 'B3', 'B4']
+    default_bands = ('B1', 'B2', 'B3', 'B4')
-    rgb_bands = ['B3', 'B2', 'B1']
+    rgb_bands = ('B3', 'B2', 'B1')
 
 
 class Landsat4TM(Landsat):
@@ -175,8 +175,8 @@ class Landsat4TM(Landsat):
 
     filename_glob = 'LT04_*_{}.*'
 
-    default_bands = ['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7']
+    default_bands = ('SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7')
-    rgb_bands = ['SR_B3', 'SR_B2', 'SR_B1']
+    rgb_bands = ('SR_B3', 'SR_B2', 'SR_B1')
 
 
 class Landsat5MSS(Landsat4MSS):
@@ -196,8 +196,8 @@ class Landsat7(Landsat):
 
     filename_glob = 'LE07_*_{}.*'
 
-    default_bands = ['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7']
+    default_bands = ('SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7')
-    rgb_bands = ['SR_B3', 'SR_B2', 'SR_B1']
+    rgb_bands = ('SR_B3', 'SR_B2', 'SR_B1')
 
 
 class Landsat8(Landsat):
@@ -205,11 +205,11 @@ class Landsat8(Landsat):
 
     filename_glob = 'LC08_*_{}.*'
 
-    default_bands = ['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7']
+    default_bands = ('SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7')
-    rgb_bands = ['SR_B4', 'SR_B3', 'SR_B2']
+    rgb_bands = ('SR_B4', 'SR_B3', 'SR_B2')
 
 
 class Landsat9(Landsat8):
-    """Landsat 9 Operational Land Imager (OLI-2) and Thermal Infrared Sensor (TIRS-2)."""  # noqa: E501
+    """Landsat 9 Operational Land Imager (OLI-2) and Thermal Infrared Sensor (TIRS-2)."""
 
     filename_glob = 'LC09_*_{}.*'

torchgeo/datasets/levircd.py

@@ -7,6 +7,7 @@ import abc
 import glob
 import os
 from collections.abc import Callable
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -26,8 +27,8 @@ class LEVIRCDBase(NonGeoDataset, abc.ABC):
     .. versionadded:: 0.6
     """
 
-    splits: list[str] | dict[str, dict[str, str]]
+    splits: ClassVar[tuple[str, ...] | dict[str, dict[str, str]]]
-    directories = ['A', 'B', 'label']
+    directories = ('A', 'B', 'label')
 
     def __init__(
         self,
@@ -237,7 +238,7 @@ class LEVIRCD(LEVIRCDBase):
     .. versionadded:: 0.6
     """
 
-    splits = {
+    splits: ClassVar[dict[str, dict[str, str]]] = {
         'train': {
             'url': 'https://drive.google.com/file/d/18GuoCuBn48oZKAlEo-LrNwABrFhVALU-',
             'filename': 'train.zip',
@@ -336,7 +337,7 @@ class LEVIRCDPlus(LEVIRCDBase):
     md5 = '1adf156f628aa32fb2e8fe6cada16c04'
     filename = 'LEVIR-CD+.zip'
     directory = 'LEVIR-CD+'
-    splits = ['train', 'test']
+    splits = ('train', 'test')
 
     def _load_files(self, root: Path, split: str) -> list[dict[str, str]]:
         """Return the paths of the files in the dataset.

torchgeo/datasets/loveda.py

@@ -5,7 +5,8 @@
 
 import glob
 import os
-from collections.abc import Callable
+from collections.abc import Callable, Sequence
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -57,10 +58,10 @@ class LoveDA(NonGeoDataset):
     .. versionadded:: 0.2
     """
 
-    scenes = ['urban', 'rural']
+    scenes = ('urban', 'rural')
-    splits = ['train', 'val', 'test']
+    splits = ('train', 'val', 'test')
 
-    info_dict = {
+    info_dict: ClassVar[dict[str, dict[str, str]]] = {
         'train': {
             'url': 'https://zenodo.org/record/5706578/files/Train.zip?download=1',
             'filename': 'Train.zip',
@@ -78,7 +79,7 @@ class LoveDA(NonGeoDataset):
         },
     }
 
-    classes = [
+    classes = (
         'background',
         'building',
         'road',
@@ -87,13 +88,13 @@ class LoveDA(NonGeoDataset):
         'forest',
         'agriculture',
         'no-data',
-    ]
+    )
 
     def __init__(
         self,
         root: Path = 'data',
         split: str = 'train',
-        scene: list[str] = ['urban', 'rural'],
+        scene: Sequence[str] = ['urban', 'rural'],
         transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
         download: bool = False,
         checksum: bool = False,

torchgeo/datasets/mapinwild.py

@@ -7,6 +7,7 @@ import os
 import shutil
 from collections import defaultdict
 from collections.abc import Callable
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -36,7 +37,7 @@ class MapInWild(NonGeoDataset):
     different RS sensors over 1018 locations: dual-pol Sentinel-1, four-season
     Sentinel-2 with 10 bands, ESA WorldCover map, and Visible Infrared Imaging
     Radiometer Suite NightTime Day/Night band. The dataset consists of 8144
-    images with the shape of 1920 × 1920 pixels. The images are weakly annotated
+    images with the shape of 1920 x 1920 pixels. The images are weakly annotated
     from the World Database of Protected Areas (WDPA).
 
     Dataset features:
@@ -54,9 +55,9 @@ class MapInWild(NonGeoDataset):
     .. versionadded:: 0.5
     """
 
-    url = 'https://hf.co/datasets/burakekim/mapinwild/resolve/d963778e31e7e0ed2329c0f4cbe493be532f0e71/'  # noqa: E501
+    url = 'https://hf.co/datasets/burakekim/mapinwild/resolve/d963778e31e7e0ed2329c0f4cbe493be532f0e71/'
 
-    modality_urls = {
+    modality_urls: ClassVar[dict[str, set[str]]] = {
         'esa_wc': {'esa_wc/ESA_WC.zip'},
         'viirs': {'viirs/VIIRS.zip'},
         'mask': {'mask/mask.zip'},
@@ -72,7 +73,7 @@ class MapInWild(NonGeoDataset):
         'split_IDs': {'split_IDs/split_IDs.csv'},
     }
 
-    md5s = {
+    md5s: ClassVar[dict[str, str]] = {
         'ESA_WC.zip': '72b2ee578fe10f0df85bdb7f19311c92',
         'VIIRS.zip': '4eff014bae127fe536f8a5f17d89ecb4',
         'mask.zip': '87c83a23a73998ad60d448d240b66225',
@@ -91,9 +92,12 @@ class MapInWild(NonGeoDataset):
         'split_IDs.csv': 'cb5c6c073702acee23544e1e6fe5856f',
     }
 
-    mask_cmap = {1: (0, 153, 0), 0: (255, 255, 255)}
+    mask_cmap: ClassVar[dict[int, tuple[int, int, int]]] = {
+        1: (0, 153, 0),
+        0: (255, 255, 255),
+    }
 
-    wc_cmap = {
+    wc_cmap: ClassVar[dict[int, tuple[int, int, int]]] = {
         10: (0, 160, 0),
         20: (150, 100, 0),
         30: (255, 180, 0),

torchgeo/datasets/millionaid.py

@@ -6,7 +6,7 @@
 import glob
 import os
 from collections.abc import Callable
-from typing import Any, cast
+from typing import Any, ClassVar, cast
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -48,7 +48,7 @@ class MillionAID(NonGeoDataset):
     .. versionadded:: 0.3
     """
 
-    multi_label_categories = [
+    multi_label_categories = (
         'agriculture_land',
         'airport_area',
         'apartment',
@@ -122,9 +122,9 @@ class MillionAID(NonGeoDataset):
         'wind_turbine',
         'woodland',
         'works',
-    ]
+    )
 
-    multi_class_categories = [
+    multi_class_categories = (
         'apartment',
         'apron',
         'bare_land',
@@ -176,17 +176,17 @@ class MillionAID(NonGeoDataset):
         'wastewater_plant',
         'wind_turbine',
         'works',
-    ]
+    )
 
-    md5s = {
+    md5s: ClassVar[dict[str, str]] = {
         'train': '1b40503cafa9b0601653ca36cd788852',
         'test': '51a63ee3eeb1351889eacff349a983d8',
     }
 
-    filenames = {'train': 'train.zip', 'test': 'test.zip'}
+    filenames: ClassVar[dict[str, str]] = {'train': 'train.zip', 'test': 'test.zip'}
 
-    tasks = ['multi-class', 'multi-label']
+    tasks = ('multi-class', 'multi-label')
-    splits = ['train', 'test']
+    splits = ('train', 'test')
 
     def __init__(
         self,

torchgeo/datasets/naip.py

@@ -45,8 +45,8 @@ class NAIP(RasterDataset):
     """
 
     # Plotting
-    all_bands = ['R', 'G', 'B', 'NIR']
+    all_bands = ('R', 'G', 'B', 'NIR')
-    rgb_bands = ['R', 'G', 'B']
+    rgb_bands = ('R', 'G', 'B')
 
     def plot(
         self,

torchgeo/datasets/nccm.py

@@ -4,7 +4,7 @@
 """Northeastern China Crop Map Dataset."""
 
 from collections.abc import Callable, Iterable
-from typing import Any
+from typing import Any, ClassVar
 
 import matplotlib.pyplot as plt
 import torch
@@ -57,23 +57,23 @@ class NCCM(RasterDataset):
 
     date_format = '%Y'
     is_image = False
-    urls = {
+    urls: ClassVar[dict[int, str]] = {
         2019: 'https://figshare.com/ndownloader/files/25070540',
         2018: 'https://figshare.com/ndownloader/files/25070624',
         2017: 'https://figshare.com/ndownloader/files/25070582',
     }
-    md5s = {
+    md5s: ClassVar[dict[int, str]] = {
         2019: '0d062bbd42e483fdc8239d22dba7020f',
         2018: 'b3bb4894478d10786aa798fb11693ec1',
         2017: 'd047fbe4a85341fa6248fd7e0badab6c',
     }
-    fnames = {
+    fnames: ClassVar[dict[int, str]] = {
         2019: 'CDL2019_clip.tif',
         2018: 'CDL2018_clip1.tif',
         2017: 'CDL2017_clip.tif',
     }
 
-    cmap = {
+    cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {
         0: (0, 255, 0, 255),
         1: (255, 0, 0, 255),
         2: (255, 255, 0, 255),

torchgeo/datasets/nlcd.py

@@ -7,7 +7,7 @@ import glob
 import os
 import pathlib
 from collections.abc import Callable, Iterable
-from typing import Any
+from typing import Any, ClassVar
 
 import matplotlib.pyplot as plt
 import torch
@@ -67,7 +67,7 @@ class NLCD(RasterDataset):
     * 2019: https://doi.org/10.5066/P9KZCM54
 
     .. versionadded:: 0.5
-    """  # noqa: E501
+    """
 
     filename_glob = 'nlcd_*_land_cover_l48_*.img'
     filename_regex = (
@@ -79,7 +79,7 @@ class NLCD(RasterDataset):
 
     url = 'https://s3-us-west-2.amazonaws.com/mrlc/nlcd_{}_land_cover_l48_20210604.zip'
 
-    md5s = {
+    md5s: ClassVar[dict[int, str]] = {
         2001: '538166a4d783204764e3df3b221fc4cd',
         2006: '67454e7874a00294adb9442374d0c309',
         2011: 'ea524c835d173658eeb6fa3c8e6b917b',
@@ -87,7 +87,7 @@ class NLCD(RasterDataset):
         2019: '82851c3f8105763b01c83b4a9e6f3961',
     }
 
-    cmap = {
+    cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {
         0: (0, 0, 0, 0),
         11: (70, 107, 159, 255),
         12: (209, 222, 248, 255),

torchgeo/datasets/openbuildings.py

@@ -9,7 +9,7 @@ import os
 import pathlib
 import sys
 from collections.abc import Callable, Iterable
-from typing import Any, cast
+from typing import Any, ClassVar, cast
 
 import fiona
 import fiona.transform
@@ -61,7 +61,7 @@ class OpenBuildings(VectorDataset):
     .. versionadded:: 0.3
     """
 
-    md5s = {
+    md5s: ClassVar[dict[str, str]] = {
         '025_buildings.csv.gz': '41db2572bfd08628d01475a2ee1a2f17',
         '04f_buildings.csv.gz': '3232c1c6d45c1543260b77e5689fc8b1',
         '05b_buildings.csv.gz': '4fc57c63bbbf9a21a3902da7adc3a670',

torchgeo/datasets/oscd.py

@@ -6,6 +6,7 @@
 import glob
 import os
 from collections.abc import Callable, Sequence
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -50,7 +51,7 @@ class OSCD(NonGeoDataset):
     .. versionadded:: 0.2
     """
 
-    urls = {
+    urls: ClassVar[dict[str, str]] = {
         'Onera Satellite Change Detection dataset - Images.zip': (
             'https://partage.imt.fr/index.php/s/gKRaWgRnLMfwMGo/download'
         ),
@@ -61,7 +62,7 @@ class OSCD(NonGeoDataset):
             'https://partage.imt.fr/index.php/s/gpStKn4Mpgfnr63/download'
         ),
     }
-    md5s = {
+    md5s: ClassVar[dict[str, str]] = {
         'Onera Satellite Change Detection dataset - Images.zip': (
             'c50d4a2941da64e03a47ac4dec63d915'
         ),
@@ -75,9 +76,9 @@ class OSCD(NonGeoDataset):
 
     zipfile_glob = '*Onera*.zip'
     filename_glob = '*Onera*'
-    splits = ['train', 'test']
+    splits = ('train', 'test')
 
-    colormap = ['blue']
+    colormap = ('blue',)
 
     all_bands = (
         'B01',
@@ -319,7 +320,7 @@ class OSCD(NonGeoDataset):
                 torch.from_numpy(rgb_img),
                 sample['mask'],
                 alpha=alpha,
-                colors=self.colormap,
+                colors=list(self.colormap),
             )
             return array
 

torchgeo/datasets/pastis.py

@@ -5,6 +5,7 @@
 
 import os
 from collections.abc import Callable, Sequence
+from typing import ClassVar
 
 import fiona
 import matplotlib.pyplot as plt
@@ -70,7 +71,7 @@ class PASTIS(NonGeoDataset):
     .. versionadded:: 0.5
     """
 
-    classes = [
+    classes = (
         'background',  # all non-agricultural land
         'meadow',
         'soft_winter_wheat',
@@ -91,8 +92,8 @@ class PASTIS(NonGeoDataset):
         'mixed_cereal',
         'sorghum',
         'void_label',  # for parcels mostly outside their patch
-    ]
+    )
-    cmap = {
+    cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {
         0: (0, 0, 0, 255),
         1: (174, 199, 232, 255),
         2: (255, 127, 14, 255),
@@ -118,7 +119,7 @@ class PASTIS(NonGeoDataset):
     filename = 'PASTIS-R.zip'
     url = 'https://zenodo.org/record/5735646/files/PASTIS-R.zip?download=1'
     md5 = '4887513d6c2d2b07fa935d325bd53e09'
-    prefix = {
+    prefix: ClassVar[dict[str, str]] = {
         's2': os.path.join('DATA_S2', 'S2_'),
         's1a': os.path.join('DATA_S1A', 'S1A_'),
         's1d': os.path.join('DATA_S1D', 'S1D_'),
@@ -232,7 +233,7 @@ class PASTIS(NonGeoDataset):
         Returns:
             the target mask
         """
-        # See https://github.com/VSainteuf/pastis-benchmark/blob/main/code/dataloader.py#L201 # noqa: E501
+        # See https://github.com/VSainteuf/pastis-benchmark/blob/main/code/dataloader.py#L201
         # even though the mask file is 3 bands, we just select the first band
         array = np.load(self.files[index]['semantic'])[0].astype(np.uint8)
         tensor = torch.from_numpy(array).long()

torchgeo/datasets/potsdam.py

@@ -5,6 +5,7 @@
 
 import os
 from collections.abc import Callable
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -54,12 +55,12 @@ class Potsdam2D(NonGeoDataset):
     * https://doi.org/10.5194/isprsannals-I-3-293-2012
 
     .. versionadded:: 0.2
-    """  # noqa: E501
+    """
 
-    filenames = ['4_Ortho_RGBIR.zip', '5_Labels_all.zip']
+    filenames = ('4_Ortho_RGBIR.zip', '5_Labels_all.zip')
-    md5s = ['c4a8f7d8c7196dd4eba4addd0aae10c1', 'cf7403c1a97c0d279414db']
+    md5s = ('c4a8f7d8c7196dd4eba4addd0aae10c1', 'cf7403c1a97c0d279414db')
     image_root = '4_Ortho_RGBIR'
-    splits = {
+    splits: ClassVar[dict[str, list[str]]] = {
         'train': [
             'top_potsdam_2_10',
             'top_potsdam_2_11',
@@ -103,22 +104,22 @@ class Potsdam2D(NonGeoDataset):
             'top_potsdam_7_13',
         ],
     }
-    classes = [
+    classes = (
         'Clutter/background',
         'Impervious surfaces',
         'Building',
         'Low Vegetation',
         'Tree',
         'Car',
-    ]
+    )
-    colormap = [
+    colormap = (
         (255, 0, 0),
         (255, 255, 255),
         (0, 0, 255),
         (0, 255, 255),
         (0, 255, 0),
         (255, 255, 0),
-    ]
+    )
 
     def __init__(
         self,
@@ -257,7 +258,7 @@ class Potsdam2D(NonGeoDataset):
         """
         ncols = 1
         image1 = draw_semantic_segmentation_masks(
-            sample['image'][:3], sample['mask'], alpha=alpha, colors=self.colormap
+            sample['image'][:3], sample['mask'], alpha=alpha, colors=list(self.colormap)
         )
         if 'prediction' in sample:
             ncols += 1
@@ -265,7 +266,7 @@ class Potsdam2D(NonGeoDataset):
                 sample['image'][:3],
                 sample['prediction'],
                 alpha=alpha,
-                colors=self.colormap,
+                colors=list(self.colormap),
             )
 
         fig, axs = plt.subplots(ncols=ncols, figsize=(ncols * 10, 10))

torchgeo/datasets/quakeset.py

@@ -5,7 +5,7 @@
 
 import os
 from collections.abc import Callable
-from typing import Any, cast
+from typing import Any, ClassVar, cast
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -61,8 +61,12 @@ class QuakeSet(NonGeoDataset):
     filename = 'earthquakes.h5'
     url = 'https://hf.co/datasets/DarthReca/quakeset/resolve/bead1d25fb9979dbf703f9ede3e8b349f73b29f7/earthquakes.h5'
     md5 = '76fc7c76b7ca56f4844d852e175e1560'
-    splits = {'train': 'train', 'val': 'validation', 'test': 'test'}
+    splits: ClassVar[dict[str, str]] = {
-    classes = ['unaffected_area', 'earthquake_affected_area']
+        'train': 'train',
+        'val': 'validation',
+        'test': 'test',
+    }
+    classes = ('unaffected_area', 'earthquake_affected_area')
 
     def __init__(
         self,

torchgeo/datasets/reforestree.py

@@ -56,7 +56,7 @@ class ReforesTree(NonGeoDataset):
     .. versionadded:: 0.3
     """
 
-    classes = ['other', 'banana', 'cacao', 'citrus', 'fruit', 'timber']
+    classes = ('other', 'banana', 'cacao', 'citrus', 'fruit', 'timber')
     url = 'https://zenodo.org/record/6813783/files/reforesTree.zip?download=1'
 
     md5 = 'f6a4a1d8207aeaa5fbab7b21b683a302'

torchgeo/datasets/resisc45.py

@@ -5,7 +5,7 @@
 
 import os
 from collections.abc import Callable
-from typing import cast
+from typing import ClassVar, cast
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -98,13 +98,13 @@ class RESISC45(NonGeoClassificationDataset):
     filename = 'NWPU-RESISC45.zip'
     directory = 'NWPU-RESISC45'
 
-    splits = ['train', 'val', 'test']
+    splits = ('train', 'val', 'test')
-    split_urls = {
+    split_urls: ClassVar[dict[str, str]] = {
         'train': 'https://hf.co/datasets/torchgeo/resisc45/resolve/a826b44d938a883185f11ebe3d512d38b464312f/resisc45-train.txt',
         'val': 'https://hf.co/datasets/torchgeo/resisc45/resolve/a826b44d938a883185f11ebe3d512d38b464312f/resisc45-val.txt',
         'test': 'https://hf.co/datasets/torchgeo/resisc45/resolve/a826b44d938a883185f11ebe3d512d38b464312f/resisc45-test.txt',
     }
-    split_md5s = {
+    split_md5s: ClassVar[dict[str, str]] = {
         'train': 'b5a4c05a37de15e4ca886696a85c403e',
         'val': 'a0770cee4c5ca20b8c32bbd61e114805',
         'test': '3dda9e4988b47eb1de9f07993653eb08',

torchgeo/datasets/rwanda_field_boundary.py

@@ -6,6 +6,7 @@
 import glob
 import os
 from collections.abc import Callable, Sequence
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -55,11 +56,11 @@ class RwandaFieldBoundary(NonGeoDataset):
 
     url = 'https://radiantearth.blob.core.windows.net/mlhub/nasa_rwanda_field_boundary_competition'
 
-    splits = {'train': 57, 'test': 13}
+    splits: ClassVar[dict[str, int]] = {'train': 57, 'test': 13}
     dates = ('2021_03', '2021_04', '2021_08', '2021_10', '2021_11', '2021_12')
     all_bands = ('B01', 'B02', 'B03', 'B04')
     rgb_bands = ('B03', 'B02', 'B01')
-    classes = ['No field-boundary', 'Field-boundary']
+    classes = ('No field-boundary', 'Field-boundary')
 
     def __init__(
         self,

torchgeo/datasets/seasonet.py

@@ -6,6 +6,7 @@
 import os
 import random
 from collections.abc import Callable, Collection, Iterable
+from typing import ClassVar
 
 import matplotlib.patches as mpatches
 import matplotlib.pyplot as plt
@@ -85,51 +86,51 @@ class SeasoNet(NonGeoDataset):
     .. versionadded:: 0.5
     """
 
-    metadata = [
+    metadata = (
         {
             'name': 'spring',
             'ext': '.zip',
-            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/spring.zip',  # noqa: E501
+            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/spring.zip',
             'md5': 'de4cdba7b6196aff624073991b187561',
         },
         {
             'name': 'summer',
             'ext': '.zip',
-            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/summer.zip',  # noqa: E501
+            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/summer.zip',
             'md5': '6a54d4e134d27ae4eb03f180ee100550',
         },
         {
             'name': 'fall',
             'ext': '.zip',
-            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/fall.zip',  # noqa: E501
+            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/fall.zip',
             'md5': '5f94920fe41a63c6bfbab7295f7d6b95',
         },
         {
             'name': 'winter',
             'ext': '.zip',
-            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/winter.zip',  # noqa: E501
+            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/winter.zip',
             'md5': 'dc5e3e09e52ab5c72421b1e3186c9a48',
         },
         {
             'name': 'snow',
             'ext': '.zip',
-            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/snow.zip',  # noqa: E501
+            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/snow.zip',
             'md5': 'e1b300994143f99ebb03f51d6ab1cbe6',
         },
         {
             'name': 'splits',
             'ext': '.zip',
-            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/splits.zip',  # noqa: E501
+            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/splits.zip',
             'md5': 'e4ec4a18bc4efc828f0944a7cf4d5fed',
         },
         {
             'name': 'meta.csv',
             'ext': '',
-            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/meta.csv',  # noqa: E501
+            'url': 'https://zenodo.org/api/files/e2288446-9ee8-4b2e-ae76-cd80366a40e1/meta.csv',
             'md5': '43ea07974936a6bf47d989c32e16afe7',
         },
-    ]
+    )
-    classes = [
+    classes = (
         'Continuous urban fabric',
         'Discontinuous urban fabric',
         'Industrial or commercial units',
@@ -163,12 +164,17 @@ class SeasoNet(NonGeoDataset):
         'Coastal lagoons',
         'Estuaries',
         'Sea and ocean',
-    ]
+    )
-    all_seasons = {'Spring', 'Summer', 'Fall', 'Winter', 'Snow'}
+    all_seasons = frozenset({'Spring', 'Summer', 'Fall', 'Winter', 'Snow'})
     all_bands = ('10m_RGB', '10m_IR', '20m', '60m')
-    band_nums = {'10m_RGB': 3, '10m_IR': 1, '20m': 6, '60m': 2}
+    band_nums: ClassVar[dict[str, int]] = {
-    splits = ['train', 'val', 'test']
+        '10m_RGB': 3,
-    cmap = {
+        '10m_IR': 1,
+        '20m': 6,
+        '60m': 2,
+    }
+    splits = ('train', 'val', 'test')
+    cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {
         0: (230, 000, 77, 255),
         1: (255, 000, 000, 255),
         2: (204, 77, 242, 255),
@@ -331,7 +337,7 @@ class SeasoNet(NonGeoDataset):
             for band in self.bands:
                 with rasterio.open(f'{path}_{band}.tif') as f:
                     array = f.read(
-                        out_shape=[f.count] + list(self.image_size),
+                        out_shape=[f.count, *list(self.image_size)],
                         out_dtype='int32',
                         resampling=Resampling.bilinear,
                     )

torchgeo/datasets/seco.py

@@ -5,7 +5,8 @@
 
 import os
 import random
-from collections.abc import Callable
+from collections.abc import Callable, Sequence
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -37,7 +38,7 @@ class SeasonalContrastS2(NonGeoDataset):
     * https://arxiv.org/pdf/2103.16607.pdf
     """
 
-    all_bands = [
+    all_bands = (
         'B1',
         'B2',
         'B3',
@@ -50,10 +51,10 @@ class SeasonalContrastS2(NonGeoDataset):
         'B9',
         'B11',
         'B12',
-    ]
+    )
-    rgb_bands = ['B4', 'B3', 'B2']
+    rgb_bands = ('B4', 'B3', 'B2')
 
-    metadata = {
+    metadata: ClassVar[dict[str, dict[str, str]]] = {
         '100k': {
             'url': 'https://zenodo.org/record/4728033/files/seco_100k.zip?download=1',
             'md5': 'ebf2d5e03adc6e657f9a69a20ad863e0',
@@ -73,7 +74,7 @@ class SeasonalContrastS2(NonGeoDataset):
         root: Path = 'data',
         version: str = '100k',
         seasons: int = 1,
-        bands: list[str] = rgb_bands,
+        bands: Sequence[str] = rgb_bands,
         transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
         download: bool = False,
         checksum: bool = False,

torchgeo/datasets/sen12ms.py

@@ -5,6 +5,7 @@
 
 import os
 from collections.abc import Callable, Sequence
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -63,9 +64,9 @@ class SEN12MS(NonGeoDataset):
        or manually downloaded from https://dataserv.ub.tum.de/s/m1474000
        and https://github.com/schmitt-muc/SEN12MS/tree/master/splits.
        This download will likely take several hours.
-    """  # noqa: E501
+    """
 
-    BAND_SETS: dict[str, tuple[str, ...]] = {
+    BAND_SETS: ClassVar[dict[str, tuple[str, ...]]] = {
         'all': (
             'VV',
             'VH',
@@ -120,9 +121,9 @@ class SEN12MS(NonGeoDataset):
         'B12',
     )
 
-    rgb_bands = ['B04', 'B03', 'B02']
+    rgb_bands = ('B04', 'B03', 'B02')
 
-    filenames = [
+    filenames = (
         'ROIs1158_spring_lc.tar.gz',
         'ROIs1158_spring_s1.tar.gz',
         'ROIs1158_spring_s2.tar.gz',
@@ -137,16 +138,16 @@ class SEN12MS(NonGeoDataset):
         'ROIs2017_winter_s2.tar.gz',
         'train_list.txt',
         'test_list.txt',
-    ]
+    )
-    light_filenames = [
+    light_filenames = (
         'ROIs1158_spring',
         'ROIs1868_summer',
         'ROIs1970_fall',
         'ROIs2017_winter',
         'train_list.txt',
         'test_list.txt',
-    ]
+    )
-    md5s = [
+    md5s = (
         '6e2e8fa8b8cba77ddab49fd20ff5c37b',
         'fba019bb27a08c1db96b31f718c34d79',
         'd58af2c15a16f376eb3308dc9b685af2',
@@ -161,7 +162,7 @@ class SEN12MS(NonGeoDataset):
         '3807545661288dcca312c9c538537b63',
         '0a68d4e1eb24f128fccdb930000b2546',
         'c7faad064001e646445c4c634169484d',
-    ]
+    )
 
     def __init__(
         self,

torchgeo/datasets/sentinel.py

@@ -137,7 +137,7 @@ class Sentinel1(Sentinel):
         \.
     """
     date_format = '%Y%m%dT%H%M%S'
-    all_bands = ['HH', 'HV', 'VV', 'VH']
+    all_bands = ('HH', 'HV', 'VV', 'VH')
     separate_files = True
 
     def __init__(
@@ -277,7 +277,7 @@ class Sentinel2(Sentinel):
     date_format = '%Y%m%dT%H%M%S'
 
     # https://gisgeography.com/sentinel-2-bands-combinations/
-    all_bands = [
+    all_bands: tuple[str, ...] = (
         'B01',
         'B02',
         'B03',
@@ -291,8 +291,8 @@ class Sentinel2(Sentinel):
         'B10',
         'B11',
         'B12',
-    ]
+    )
-    rgb_bands = ['B04', 'B03', 'B02']
+    rgb_bands = ('B04', 'B03', 'B02')
 
     separate_files = True
 

torchgeo/datasets/skippd.py

@@ -5,7 +5,7 @@
 
 import os
 from collections.abc import Callable
-from typing import Any
+from typing import Any, ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -62,8 +62,8 @@ class SKIPPD(NonGeoDataset):
     .. versionadded:: 0.5
     """
 
-    url = 'https://hf.co/datasets/torchgeo/skippd/resolve/a16c7e200b4618cd93be3143cdb973e3f21498fa/{}'  # noqa: E501
+    url = 'https://hf.co/datasets/torchgeo/skippd/resolve/a16c7e200b4618cd93be3143cdb973e3f21498fa/{}'
-    md5 = {
+    md5: ClassVar[dict[str, str]] = {
         'forecast': 'f4f3509ddcc83a55c433be9db2e51077',
         'nowcast': '0000761d403e45bb5f86c21d3c69aa80',
     }
@@ -71,9 +71,9 @@ class SKIPPD(NonGeoDataset):
     data_file_name = '2017_2019_images_pv_processed_{}.hdf5'
     zipfile_name = '2017_2019_images_pv_processed_{}.zip'
 
-    valid_splits = ['trainval', 'test']
+    valid_splits = ('trainval', 'test')
 
-    valid_tasks = ['nowcast', 'forecast']
+    valid_tasks = ('nowcast', 'forecast')
 
     dateformat = '%m/%d/%Y, %H:%M:%S'
 

torchgeo/datasets/so2sat.py

@@ -5,7 +5,7 @@
 
 import os
 from collections.abc import Callable, Sequence
-from typing import cast
+from typing import ClassVar, cast
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -103,10 +103,10 @@ class So2Sat(NonGeoDataset):
        This dataset requires the following additional library to be installed:
 
        * `<https://pypi.org/project/h5py/>`_ to load the dataset
-    """  # noqa: E501
+    """
 
-    versions = ['2', '3_random', '3_block', '3_culture_10']
+    versions = ('2', '3_random', '3_block', '3_culture_10')
-    filenames_by_version = {
+    filenames_by_version: ClassVar[dict[str, dict[str, str]]] = {
         '2': {
             'train': 'training.h5',
             'validation': 'validation.h5',
@@ -119,7 +119,7 @@ class So2Sat(NonGeoDataset):
             'test': 'culture_10/testing.h5',
         },
     }
-    md5s_by_version = {
+    md5s_by_version: ClassVar[dict[str, dict[str, str]]] = {
         '2': {
             'train': '702bc6a9368ebff4542d791e53469244',
             'validation': '71cfa6795de3e22207229d06d6f8775d',
@@ -139,7 +139,7 @@ class So2Sat(NonGeoDataset):
         },
     }
 
-    classes = [
+    classes = (
         'Compact high rise',
         'Compact mid rise',
         'Compact low rise',
@@ -157,7 +157,7 @@ class So2Sat(NonGeoDataset):
         'Bare rock or paved',
         'Bare soil or sand',
         'Water',
-    ]
+    )
 
     all_s1_band_names = (
         'S1_B1',
@@ -183,9 +183,9 @@ class So2Sat(NonGeoDataset):
     )
     all_band_names = all_s1_band_names + all_s2_band_names
 
-    rgb_bands = ['S2_B04', 'S2_B03', 'S2_B02']
+    rgb_bands = ('S2_B04', 'S2_B03', 'S2_B02')
 
-    BAND_SETS = {
+    BAND_SETS: ClassVar[dict[str, tuple[str, ...]]] = {
         'all': all_band_names,
         's1': all_s1_band_names,
         's2': all_s2_band_names,

torchgeo/datasets/south_africa_crop_type.py

@@ -6,8 +6,8 @@
 import os
 import pathlib
 import re
-from collections.abc import Callable, Iterable
+from collections.abc import Callable, Iterable, Sequence
-from typing import Any, cast
+from typing import Any, ClassVar, cast
 
 import matplotlib.pyplot as plt
 import torch
@@ -79,9 +79,9 @@ class SouthAfricaCropType(RasterDataset):
         _10m
     """
     date_format = '%Y_%m_%d'
-    rgb_bands = ['B04', 'B03', 'B02']
+    rgb_bands = ('B04', 'B03', 'B02')
-    s1_bands = ['VH', 'VV']
+    s1_bands = ('VH', 'VV')
-    s2_bands = [
+    s2_bands = (
         'B01',
         'B02',
         'B03',
@@ -94,9 +94,9 @@ class SouthAfricaCropType(RasterDataset):
         'B09',
         'B11',
         'B12',
-    ]
+    )
-    all_bands: list[str] = s1_bands + s2_bands
+    all_bands = s1_bands + s2_bands
-    cmap = {
+    cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {
         0: (0, 0, 0, 255),
         1: (255, 211, 0, 255),
         2: (255, 37, 37, 255),
@@ -113,8 +113,8 @@ class SouthAfricaCropType(RasterDataset):
         self,
         paths: Path | Iterable[Path] = 'data',
         crs: CRS | None = None,
-        classes: list[int] = list(cmap.keys()),
+        classes: Sequence[int] = list(cmap.keys()),
-        bands: list[str] = s2_bands,
+        bands: Sequence[str] = s2_bands,
         transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
         download: bool = False,
     ) -> None:

torchgeo/datasets/south_america_soybean.py

@@ -5,7 +5,7 @@
 
 import pathlib
 from collections.abc import Callable, Iterable
-from typing import Any
+from typing import Any, ClassVar
 
 import matplotlib.pyplot as plt
 from matplotlib.figure import Figure
@@ -47,7 +47,7 @@ class SouthAmericaSoybean(RasterDataset):
     is_image = False
     url = 'https://glad.umd.edu/projects/AnnualClassMapsV1/SouthAmerica_Soybean_{}.tif'
 
-    md5s = {
+    md5s: ClassVar[dict[int, str]] = {
         2021: 'edff3ada13a1a9910d1fe844d28ae4f',
         2020: '0709dec807f576c9707c8c7e183db31',
         2019: '441836493bbcd5e123cff579a58f5a4f',

torchgeo/datasets/spacenet.py

@@ -8,7 +8,7 @@ import os
 import re
 from abc import ABC, abstractmethod
 from collections.abc import Callable
-from typing import Any
+from typing import Any, ClassVar
 
 import fiona
 import matplotlib.pyplot as plt
@@ -55,9 +55,9 @@ class SpaceNet(NonGeoDataset, ABC):
     image_glob = '*.tif'
     mask_glob = '*.geojson'
     file_regex = r'_img(\d+)\.'
-    chip_size: dict[str, tuple[int, int]] = {}
+    chip_size: ClassVar[dict[str, tuple[int, int]]] = {}
 
-    cities = {
+    cities: ClassVar[dict[int, str]] = {
         1: 'Rio',
         2: 'Vegas',
         3: 'Paris',
@@ -98,7 +98,7 @@ class SpaceNet(NonGeoDataset, ABC):
 
     @property
     @abstractmethod
-    def valid_masks(self) -> list[str]:
+    def valid_masks(self) -> tuple[str, ...]:
         """List of valid masks."""
 
     def __init__(
@@ -426,7 +426,7 @@ class SpaceNet1(SpaceNet):
 
     directory_glob = '{product}'
     dataset_id = 'SN1_buildings'
-    tarballs = {
+    tarballs: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {
             1: [
                 'SN1_buildings_train_AOI_1_Rio_3band.tar.gz',
@@ -441,7 +441,7 @@ class SpaceNet1(SpaceNet):
             ]
         },
     }
-    md5s = {
+    md5s: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {
             1: [
                 '279e334a2120ecac70439ea246174516',
@@ -453,10 +453,16 @@ class SpaceNet1(SpaceNet):
             1: ['18283d78b21c239bc1831f3bf1d2c996', '732b3a40603b76e80aac84e002e2b3e8']
         },
     }
-    valid_aois = {'train': [1], 'test': [1]}
+    valid_aois: ClassVar[dict[str, list[int]]] = {'train': [1], 'test': [1]}
-    valid_images = {'train': ['3band', '8band'], 'test': ['3band', '8band']}
+    valid_images: ClassVar[dict[str, list[str]]] = {
-    valid_masks = ['geojson']
+        'train': ['3band', '8band'],
-    chip_size = {'3band': (406, 439), '8band': (102, 110)}
+        'test': ['3band', '8band'],
+    }
+    valid_masks = ('geojson',)
+    chip_size: ClassVar[dict[str, tuple[int, int]]] = {
+        '3band': (406, 439),
+        '8band': (102, 110),
+    }
 
 
 class SpaceNet2(SpaceNet):
@@ -522,7 +528,7 @@ class SpaceNet2(SpaceNet):
     """
 
     dataset_id = 'SN2_buildings'
-    tarballs = {
+    tarballs: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {
             2: ['SN2_buildings_train_AOI_2_Vegas.tar.gz'],
             3: ['SN2_buildings_train_AOI_3_Paris.tar.gz'],
@@ -536,7 +542,7 @@ class SpaceNet2(SpaceNet):
             5: ['AOI_5_Khartoum_Test_public.tar.gz'],
         },
     }
-    md5s = {
+    md5s: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {
             2: ['307da318bc43aaf9481828f92eda9126'],
             3: ['4db469e3e4e7bf025368ad730aec0888'],
@@ -550,13 +556,16 @@ class SpaceNet2(SpaceNet):
             5: ['037d7be10530f0dd1c43d4ef79f3236e'],
         },
     }
-    valid_aois = {'train': [2, 3, 4, 5], 'test': [2, 3, 4, 5]}
+    valid_aois: ClassVar[dict[str, list[int]]] = {
-    valid_images = {
+        'train': [2, 3, 4, 5],
+        'test': [2, 3, 4, 5],
+    }
+    valid_images: ClassVar[dict[str, list[str]]] = {
         'train': ['MUL', 'MUL-PanSharpen', 'PAN', 'RGB-PanSharpen'],
         'test': ['MUL', 'MUL-PanSharpen', 'PAN', 'RGB-PanSharpen'],
     }
-    valid_masks = [os.path.join('geojson', 'buildings')]
+    valid_masks = (os.path.join('geojson', 'buildings'),)
-    chip_size = {'MUL': (163, 163)}
+    chip_size: ClassVar[dict[str, tuple[int, int]]] = {'MUL': (163, 163)}
 
 
 class SpaceNet3(SpaceNet):
@@ -624,7 +633,7 @@ class SpaceNet3(SpaceNet):
     """
 
     dataset_id = 'SN3_roads'
-    tarballs = {
+    tarballs: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {
             2: [
                 'SN3_roads_train_AOI_2_Vegas.tar.gz',
@@ -650,7 +659,7 @@ class SpaceNet3(SpaceNet):
             5: ['SN3_roads_test_public_AOI_5_Khartoum.tar.gz'],
         },
     }
-    md5s = {
+    md5s: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {
             2: ['06317255b5e0c6df2643efd8a50f22ae', '4acf7846ed8121db1319345cfe9fdca9'],
             3: ['c13baf88ee10fe47870c303223cabf82', 'abc8199d4c522d3a14328f4f514702ad'],
@@ -664,12 +673,15 @@ class SpaceNet3(SpaceNet):
             5: ['f367c79fa0fc1d38e63a0fdd065ed957'],
         },
     }
-    valid_aois = {'train': [2, 3, 4, 5], 'test': [2, 3, 4, 5]}
+    valid_aois: ClassVar[dict[str, list[int]]] = {
-    valid_images = {
+        'train': [2, 3, 4, 5],
+        'test': [2, 3, 4, 5],
+    }
+    valid_images: ClassVar[dict[str, list[str]]] = {
         'train': ['MS', 'PS-MS', 'PAN', 'PS-RGB'],
         'test': ['MUL', 'MUL-PanSharpen', 'PAN', 'RGB-PanSharpen'],
     }
-    valid_masks = ['geojson_roads', 'geojson_roads_speed']
+    valid_masks: tuple[str, ...] = ('geojson_roads', 'geojson_roads_speed')
 
 
 class SpaceNet4(SpaceNet):
@@ -708,7 +720,7 @@ class SpaceNet4(SpaceNet):
     directory_glob = os.path.join('**', '{product}')
     file_regex = r'_(\d+_\d+)\.'
     dataset_id = 'SN4_buildings'
-    tarballs = {
+    tarballs: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {
             6: [
                 'Atlanta_nadir7_catid_1030010003D22F00.tar.gz',
@@ -743,7 +755,7 @@ class SpaceNet4(SpaceNet):
         },
         'test': {6: ['SN4_buildings_AOI_6_Atlanta_test_public.tar.gz']},
     }
-    md5s = {
+    md5s: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {
             6: [
                 'd41ab6ec087b07e1e046c55d1fa5754b',
@@ -778,12 +790,12 @@ class SpaceNet4(SpaceNet):
         },
         'test': {6: ['0ec3874bfc19aed63b33ac47b039aace']},
     }
-    valid_aois = {'train': [6], 'test': [6]}
+    valid_aois: ClassVar[dict[str, list[int]]] = {'train': [6], 'test': [6]}
-    valid_images = {
+    valid_images: ClassVar[dict[str, list[str]]] = {
         'train': ['MS', 'PAN', 'Pan-Sharpen'],
         'test': ['MS', 'PAN', 'Pan-Sharpen'],
     }
-    valid_masks = [os.path.join('geojson', 'spacenet-buildings')]
+    valid_masks = (os.path.join('geojson', 'spacenet-buildings'),)
 
 
 class SpaceNet5(SpaceNet3):
@@ -850,26 +862,26 @@ class SpaceNet5(SpaceNet3):
 
     file_regex = r'_chip(\d+)\.'
     dataset_id = 'SN5_roads'
-    tarballs = {
+    tarballs: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {
             7: ['SN5_roads_train_AOI_7_Moscow.tar.gz'],
             8: ['SN5_roads_train_AOI_8_Mumbai.tar.gz'],
         },
         'test': {9: ['SN5_roads_test_public_AOI_9_San_Juan.tar.gz']},
     }
-    md5s = {
+    md5s: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {
             7: ['03082d01081a6d8df2bc5a9645148d2a'],
             8: ['1ee20ba781da6cb7696eef9a95a5bdcc'],
         },
         'test': {9: ['fc45afef219dfd3a20f2d4fc597f6882']},
     }
-    valid_aois = {'train': [7, 8], 'test': [9]}
+    valid_aois: ClassVar[dict[str, list[int]]] = {'train': [7, 8], 'test': [9]}
-    valid_images = {
+    valid_images: ClassVar[dict[str, list[str]]] = {
         'train': ['MS', 'PAN', 'PS-MS', 'PS-RGB'],
         'test': ['MS', 'PAN', 'PS-MS', 'PS-RGB'],
     }
-    valid_masks = ['geojson_roads_speed']
+    valid_masks = ('geojson_roads_speed',)
 
 
 class SpaceNet6(SpaceNet):
@@ -937,20 +949,20 @@ class SpaceNet6(SpaceNet):
 
     file_regex = r'_tile_(\d+)\.'
     dataset_id = 'SN6_buildings'
-    tarballs = {
+    tarballs: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {11: ['SN6_buildings_AOI_11_Rotterdam_train.tar.gz']},
         'test': {11: ['SN6_buildings_AOI_11_Rotterdam_test_public.tar.gz']},
     }
-    md5s = {
+    md5s: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {11: ['10ca26d2287716e3b6ef0cf0ad9f946e']},
         'test': {11: ['a07823a5e536feeb8bb6b6f0cb43cf05']},
     }
-    valid_aois = {'train': [11], 'test': [11]}
+    valid_aois: ClassVar[dict[str, list[int]]] = {'train': [11], 'test': [11]}
-    valid_images = {
+    valid_images: ClassVar[dict[str, list[str]]] = {
         'train': ['PAN', 'PS-RGB', 'PS-RGBNIR', 'RGBNIR', 'SAR-Intensity'],
         'test': ['SAR-Intensity'],
     }
-    valid_masks = ['geojson_buildings']
+    valid_masks = ('geojson_buildings',)
 
 
 class SpaceNet7(SpaceNet):
@@ -958,7 +970,7 @@ class SpaceNet7(SpaceNet):
 
     `SpaceNet 7 <https://spacenet.ai/sn7-challenge/>`_ is a dataset which
     consist of medium resolution (4.0m) satellite imagery mosaics acquired from
-    Planet Labs’ Dove constellation between 2017 and 2020. It includes ≈ 24
+    Planet Labs' Dove constellation between 2017 and 2020. It includes ≈ 24
     images (one per month) covering > 100 unique geographies, and comprises >
     40,000 km2 of imagery and exhaustive polygon labels of building footprints
     therein, totaling over 11M individual annotations.
@@ -993,18 +1005,24 @@ class SpaceNet7(SpaceNet):
     mask_glob = '*_Buildings.geojson'
     file_regex = r'global_monthly_(\d+.*\d+)'
     dataset_id = 'SN7_buildings'
-    tarballs = {
+    tarballs: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {0: ['SN7_buildings_train.tar.gz']},
         'test': {0: ['SN7_buildings_test_public.tar.gz']},
     }
-    md5s = {
+    md5s: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {0: ['6eda13b9c28f6f5cdf00a7e8e218c1b1']},
         'test': {0: ['b3bde95a0f8f32f3bfeba49464b9bc97']},
     }
-    valid_aois = {'train': [0], 'test': [0]}
+    valid_aois: ClassVar[dict[str, list[int]]] = {'train': [0], 'test': [0]}
-    valid_images = {'train': ['images', 'images_masked'], 'test': ['images_masked']}
+    valid_images: ClassVar[dict[str, list[str]]] = {
-    valid_masks = ['labels', 'labels_match', 'labels_match_pix']
+        'train': ['images', 'images_masked'],
-    chip_size = {'images': (1024, 1024), 'images_masked': (1024, 1024)}
+        'test': ['images_masked'],
+    }
+    valid_masks = ('labels', 'labels_match', 'labels_match_pix')
+    chip_size: ClassVar[dict[str, tuple[int, int]]] = {
+        'images': (1024, 1024),
+        'images_masked': (1024, 1024),
+    }
 
 
 class SpaceNet8(SpaceNet):
@@ -1024,7 +1042,7 @@ class SpaceNet8(SpaceNet):
     directory_glob = '{product}'
     file_regex = r'(\d+_\d+_\d+)\.'
     dataset_id = 'SN8_floods'
-    tarballs = {
+    tarballs: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {
             0: [
                 'Germany_Training_Public.tar.gz',
@@ -1033,16 +1051,19 @@ class SpaceNet8(SpaceNet):
         },
         'test': {0: ['Louisiana-West_Test_Public.tar.gz']},
     }
-    md5s = {
+    md5s: ClassVar[dict[str, dict[int, list[str]]]] = {
         'train': {
             0: ['81383a9050b93e8f70c8557d4568e8a2', 'fa40ae3cf6ac212c90073bf93d70bd95']
         },
         'test': {0: ['d41d8cd98f00b204e9800998ecf8427e']},
     }
-    valid_aois = {'train': [0], 'test': [0]}
+    valid_aois: ClassVar[dict[str, list[int]]] = {'train': [0], 'test': [0]}
-    valid_images = {
+    valid_images: ClassVar[dict[str, list[str]]] = {
         'train': ['PRE-event', 'POST-event'],
         'test': ['PRE-event', 'POST-event'],
     }
-    valid_masks = ['annotations']
+    valid_masks = ('annotations',)
-    chip_size = {'PRE-event': (1300, 1300), 'POST-event': (1300, 1300)}
+    chip_size: ClassVar[dict[str, tuple[int, int]]] = {
+        'PRE-event': (1300, 1300),
+        'POST-event': (1300, 1300),
+    }

torchgeo/datasets/ssl4eo.py

@@ -7,7 +7,7 @@ import glob
 import os
 import random
 from collections.abc import Callable
-from typing import TypedDict
+from typing import ClassVar, TypedDict
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -93,13 +93,13 @@ class SSL4EOL(NonGeoDataset):
     * https://proceedings.neurips.cc/paper_files/paper/2023/hash/bbf7ee04e2aefec136ecf60e346c2e61-Abstract-Datasets_and_Benchmarks.html
 
     .. versionadded:: 0.5
-    """  # noqa: E501
+    """
 
     class _Metadata(TypedDict):
         num_bands: int
         rgb_bands: list[int]
 
-    metadata: dict[str, _Metadata] = {
+    metadata: ClassVar[dict[str, _Metadata]] = {
         'tm_toa': {'num_bands': 7, 'rgb_bands': [2, 1, 0]},
         'etm_toa': {'num_bands': 9, 'rgb_bands': [2, 1, 0]},
         'etm_sr': {'num_bands': 6, 'rgb_bands': [2, 1, 0]},
@@ -107,8 +107,8 @@ class SSL4EOL(NonGeoDataset):
         'oli_sr': {'num_bands': 7, 'rgb_bands': [3, 2, 1]},
     }
 
-    url = 'https://hf.co/datasets/torchgeo/ssl4eo_l/resolve/e2467887e6a6bcd7547d9d5999f8d9bc3323dc31/{0}/ssl4eo_l_{0}.tar.gz{1}'  # noqa: E501
+    url = 'https://hf.co/datasets/torchgeo/ssl4eo_l/resolve/e2467887e6a6bcd7547d9d5999f8d9bc3323dc31/{0}/ssl4eo_l_{0}.tar.gz{1}'
-    checksums = {
+    checksums: ClassVar[dict[str, dict[str, str]]] = {
         'tm_toa': {
             'aa': '553795b8d73aa253445b1e67c5b81f11',
             'ab': 'e9e0739b5171b37d16086cb89ab370e8',
@@ -357,7 +357,7 @@ class SSL4EOS12(NonGeoDataset):
         md5: str
         bands: list[str]
 
-    metadata: dict[str, _Metadata] = {
+    metadata: ClassVar[dict[str, _Metadata]] = {
         's1': {
             'filename': 's1.tar.gz',
             'md5': '51ee23b33eb0a2f920bda25225072f3a',

torchgeo/datasets/ssl4eo_benchmark.py

@@ -6,6 +6,7 @@
 import glob
 import os
 from collections.abc import Callable
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -46,16 +47,16 @@ class SSL4EOLBenchmark(NonGeoDataset):
     * https://proceedings.neurips.cc/paper_files/paper/2023/hash/bbf7ee04e2aefec136ecf60e346c2e61-Abstract-Datasets_and_Benchmarks.html
 
     .. versionadded:: 0.5
-    """  # noqa: E501
+    """
 
-    url = 'https://hf.co/datasets/torchgeo/ssl4eo-l-benchmark/resolve/da96ae2b04cb509710b72fce9131c2a3d5c211c2/{}.tar.gz'  # noqa: E501
+    url = 'https://hf.co/datasets/torchgeo/ssl4eo-l-benchmark/resolve/da96ae2b04cb509710b72fce9131c2a3d5c211c2/{}.tar.gz'
 
-    valid_sensors = ['tm_toa', 'etm_toa', 'etm_sr', 'oli_tirs_toa', 'oli_sr']
+    valid_sensors = ('tm_toa', 'etm_toa', 'etm_sr', 'oli_tirs_toa', 'oli_sr')
-    valid_products = ['cdl', 'nlcd']
+    valid_products = ('cdl', 'nlcd')
-    valid_splits = ['train', 'val', 'test']
+    valid_splits = ('train', 'val', 'test')
 
     image_root = 'ssl4eo_l_{}_benchmark'
-    img_md5s = {
+    img_md5s: ClassVar[dict[str, str]] = {
         'tm_toa': '8e3c5bcd56d3780a442f1332013b8d15',
         'etm_toa': '1b051c7fe4d61c581b341370c9e76f1f',
         'etm_sr': '34a24fa89a801654f8d01e054662c8cd',
@@ -63,14 +64,14 @@ class SSL4EOLBenchmark(NonGeoDataset):
         'oli_sr': '0700cd15cc2366fe68c2f8c02fa09a15',
     }
 
-    mask_dir_dict = {
+    mask_dir_dict: ClassVar[dict[str, str]] = {
         'tm_toa': 'ssl4eo_l_tm_{}',
         'etm_toa': 'ssl4eo_l_etm_{}',
         'etm_sr': 'ssl4eo_l_etm_{}',
         'oli_tirs_toa': 'ssl4eo_l_oli_{}',
         'oli_sr': 'ssl4eo_l_oli_{}',
     }
-    mask_md5s = {
+    mask_md5s: ClassVar[dict[str, dict[str, str]]] = {
         'tm': {
             'cdl': '3d676770ffb56c7e222a7192a652a846',
             'nlcd': '261149d7614fcfdcb3be368eefa825c7',
@@ -85,7 +86,7 @@ class SSL4EOLBenchmark(NonGeoDataset):
         },
     }
 
-    year_dict = {
+    year_dict: ClassVar[dict[str, int]] = {
         'tm_toa': 2011,
         'etm_toa': 2019,
         'etm_sr': 2019,
@@ -93,7 +94,7 @@ class SSL4EOLBenchmark(NonGeoDataset):
         'oli_sr': 2019,
     }
 
-    rgb_indices = {
+    rgb_indices: ClassVar[dict[str, list[int]]] = {
         'tm_toa': [2, 1, 0],
         'etm_toa': [2, 1, 0],
         'etm_sr': [2, 1, 0],
@@ -101,9 +102,12 @@ class SSL4EOLBenchmark(NonGeoDataset):
         'oli_sr': [3, 2, 1],
     }
 
-    split_percentages = [0.7, 0.15, 0.15]
+    split_percentages = (0.7, 0.15, 0.15)
 
-    cmaps = {'nlcd': NLCD.cmap, 'cdl': CDL.cmap}
+    cmaps: ClassVar[dict[str, dict[int, tuple[int, int, int, int]]]] = {
+        'nlcd': NLCD.cmap,
+        'cdl': CDL.cmap,
+    }
 
     def __init__(
         self,

torchgeo/datasets/sustainbench_crop_yield.py

@@ -45,17 +45,17 @@ class SustainBenchCropYield(NonGeoDataset):
     * https://doi.org/10.1609/aaai.v31i1.11172
 
     .. versionadded:: 0.5
-    """  # noqa: E501
+    """
 
-    valid_countries = ['usa', 'brazil', 'argentina']
+    valid_countries = ('usa', 'brazil', 'argentina')
 
     md5 = '362bad07b51a1264172b8376b39d1fc9'
 
-    url = 'https://drive.google.com/file/d/1lhbmICpmNuOBlaErywgiD6i9nHuhuv0A/view?usp=drive_link'  # noqa: E501
+    url = 'https://drive.google.com/file/d/1lhbmICpmNuOBlaErywgiD6i9nHuhuv0A/view?usp=drive_link'
 
     dir = 'soybeans'
 
-    valid_splits = ['train', 'dev', 'test']
+    valid_splits = ('train', 'dev', 'test')
 
     def __init__(
         self,

torchgeo/datasets/ucmerced.py

@@ -5,7 +5,7 @@
 
 import os
 from collections.abc import Callable
-from typing import cast
+from typing import ClassVar, cast
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -66,19 +66,19 @@ class UCMerced(NonGeoClassificationDataset):
     * https://dl.acm.org/doi/10.1145/1869790.1869829
     """
 
-    url = 'https://hf.co/datasets/torchgeo/ucmerced/resolve/d0af6e2eeea2322af86078068bd83337148a2149/UCMerced_LandUse.zip'  # noqa: E501
+    url = 'https://hf.co/datasets/torchgeo/ucmerced/resolve/d0af6e2eeea2322af86078068bd83337148a2149/UCMerced_LandUse.zip'
     filename = 'UCMerced_LandUse.zip'
     md5 = '5b7ec56793786b6dc8a908e8854ac0e4'
 
     base_dir = os.path.join('UCMerced_LandUse', 'Images')
 
-    splits = ['train', 'val', 'test']
+    splits = ('train', 'val', 'test')
-    split_urls = {
+    split_urls: ClassVar[dict[str, str]] = {
-        'train': 'https://storage.googleapis.com/remote_sensing_representations/uc_merced-train.txt',  # noqa: E501
+        'train': 'https://storage.googleapis.com/remote_sensing_representations/uc_merced-train.txt',
-        'val': 'https://storage.googleapis.com/remote_sensing_representations/uc_merced-val.txt',  # noqa: E501
+        'val': 'https://storage.googleapis.com/remote_sensing_representations/uc_merced-val.txt',
-        'test': 'https://storage.googleapis.com/remote_sensing_representations/uc_merced-test.txt',  # noqa: E501
+        'test': 'https://storage.googleapis.com/remote_sensing_representations/uc_merced-test.txt',
     }
-    split_md5s = {
+    split_md5s: ClassVar[dict[str, str]] = {
         'train': 'f2fb12eb2210cfb53f93f063a35ff374',
         'val': '11ecabfc52782e5ea6a9c7c0d263aca0',
         'test': '046aff88472d8fc07c4678d03749e28d',

torchgeo/datasets/usavars.py

@@ -6,6 +6,7 @@
 import glob
 import os
 from collections.abc import Callable, Sequence
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -49,12 +50,12 @@ class USAVars(NonGeoDataset):
     .. versionadded:: 0.3
     """
 
-    data_url = 'https://hf.co/datasets/torchgeo/usavars/resolve/01377abfaf50c0cc8548aaafb79533666bbf288f/{}'  # noqa: E501
+    data_url = 'https://hf.co/datasets/torchgeo/usavars/resolve/01377abfaf50c0cc8548aaafb79533666bbf288f/{}'
     dirname = 'uar'
 
     md5 = '677e89fd20e5dd0fe4d29b61827c2456'
 
-    label_urls = {
+    label_urls: ClassVar[dict[str, str]] = {
         'housing': data_url.format('housing.csv'),
         'income': data_url.format('income.csv'),
         'roads': data_url.format('roads.csv'),
@@ -64,7 +65,7 @@ class USAVars(NonGeoDataset):
         'treecover': data_url.format('treecover.csv'),
     }
 
-    split_metadata = {
+    split_metadata: ClassVar[dict[str, dict[str, str]]] = {
         'train': {
             'url': data_url.format('train_split.txt'),
             'filename': 'train_split.txt',
@@ -82,7 +83,7 @@ class USAVars(NonGeoDataset):
         },
     }
 
-    ALL_LABELS = ['treecover', 'elevation', 'population']
+    ALL_LABELS = ('treecover', 'elevation', 'population')
 
     def __init__(
         self,

torchgeo/datasets/utils.py

@@ -86,11 +86,11 @@ class BoundingBox:
 
     # https://github.com/PyCQA/pydocstyle/issues/525
     @overload
-    def __getitem__(self, key: int) -> float:  # noqa: D105
+    def __getitem__(self, key: int) -> float:
         pass
 
     @overload
-    def __getitem__(self, key: slice) -> list[float]:  # noqa: D105
+    def __getitem__(self, key: slice) -> list[float]:
         pass
 
     def __getitem__(self, key: int | slice) -> float | list[float]:
@@ -289,7 +289,7 @@ class Executable:
             The completed process.
         """
         kwargs['check'] = True
-        return subprocess.run((self.name,) + args, **kwargs)
+        return subprocess.run((self.name, *args), **kwargs)
 
 
 def disambiguate_timestamp(date_str: str, format: str) -> tuple[float, float]:
@@ -547,7 +547,7 @@ def draw_semantic_segmentation_masks(
 
 
 def rgb_to_mask(
-    rgb: np.typing.NDArray[np.uint8], colors: list[tuple[int, int, int]]
+    rgb: np.typing.NDArray[np.uint8], colors: Sequence[tuple[int, int, int]]
 ) -> np.typing.NDArray[np.uint8]:
     """Converts an RGB colormap mask to a integer mask.
 

torchgeo/datasets/vaihingen.py

@@ -5,6 +5,7 @@
 
 import os
 from collections.abc import Callable
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -55,15 +56,15 @@ class Vaihingen2D(NonGeoDataset):
     * https://doi.org/10.5194/isprsannals-I-3-293-2012
 
     .. versionadded:: 0.2
-    """  # noqa: E501
+    """
 
-    filenames = [
+    filenames = (
         'ISPRS_semantic_labeling_Vaihingen.zip',
         'ISPRS_semantic_labeling_Vaihingen_ground_truth_COMPLETE.zip',
-    ]
+    )
-    md5s = ['462b8dca7b6fa9eaf729840f0cdfc7f3', '4802dd6326e2727a352fb735be450277']
+    md5s = ('462b8dca7b6fa9eaf729840f0cdfc7f3', '4802dd6326e2727a352fb735be450277')
     image_root = 'top'
-    splits = {
+    splits: ClassVar[dict[str, list[str]]] = {
         'train': [
             'top_mosaic_09cm_area1.tif',
             'top_mosaic_09cm_area11.tif',
@@ -102,22 +103,22 @@ class Vaihingen2D(NonGeoDataset):
             'top_mosaic_09cm_area29.tif',
         ],
     }
-    classes = [
+    classes = (
         'Clutter/background',
         'Impervious surfaces',
         'Building',
         'Low Vegetation',
         'Tree',
         'Car',
-    ]
+    )
-    colormap = [
+    colormap = (
         (255, 0, 0),
         (255, 255, 255),
         (0, 0, 255),
         (0, 255, 255),
         (0, 255, 0),
         (255, 255, 0),
-    ]
+    )
 
     def __init__(
         self,
@@ -258,7 +259,7 @@ class Vaihingen2D(NonGeoDataset):
         """
         ncols = 1
         image1 = draw_semantic_segmentation_masks(
-            sample['image'][:3], sample['mask'], alpha=alpha, colors=self.colormap
+            sample['image'][:3], sample['mask'], alpha=alpha, colors=list(self.colormap)
         )
         if 'prediction' in sample:
             ncols += 1
@@ -266,7 +267,7 @@ class Vaihingen2D(NonGeoDataset):
                 sample['image'][:3],
                 sample['prediction'],
                 alpha=alpha,
-                colors=self.colormap,
+                colors=list(self.colormap),
             )
 
         fig, axs = plt.subplots(ncols=ncols, figsize=(ncols * 10, 10))

torchgeo/datasets/vhr10.py

@@ -5,7 +5,7 @@
 
 import os
 from collections.abc import Callable
-from typing import Any
+from typing import Any, ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -158,18 +158,18 @@ class VHR10(NonGeoDataset):
          ``annotations.json`` file for the "positive" image set
     """
 
-    image_meta = {
+    image_meta: ClassVar[dict[str, str]] = {
         'url': 'https://hf.co/datasets/torchgeo/vhr10/resolve/7e7968ad265dadc4494e0ca4a079e0b63dc6f3f8/NWPU%20VHR-10%20dataset.zip',
         'filename': 'NWPU VHR-10 dataset.zip',
         'md5': '6add6751469c12dd8c8d6223064c6c4d',
     }
-    target_meta = {
+    target_meta: ClassVar[dict[str, str]] = {
         'url': 'https://hf.co/datasets/torchgeo/vhr10/resolve/7e7968ad265dadc4494e0ca4a079e0b63dc6f3f8/annotations.json',
         'filename': 'annotations.json',
         'md5': '7c76ec50c17a61bb0514050d20f22c08',
     }
 
-    categories = [
+    categories = (
         'background',
         'airplane',
         'ships',
@@ -181,7 +181,7 @@ class VHR10(NonGeoDataset):
         'harbor',
         'bridge',
         'vehicle',
-    ]
+    )
 
     def __init__(
         self,

torchgeo/datasets/western_usa_live_fuel_moisture.py

@@ -6,7 +6,7 @@
 import glob
 import json
 import os
-from collections.abc import Callable
+from collections.abc import Callable, Iterable
 from typing import Any
 
 import pandas as pd
@@ -53,7 +53,7 @@ class WesternUSALiveFuelMoisture(NonGeoDataset):
 
     label_name = 'percent(t)'
 
-    all_variable_names = [
+    all_variable_names = (
         # "date",
         'slope(t)',
         'elevation(t)',
@@ -193,12 +193,12 @@ class WesternUSALiveFuelMoisture(NonGeoDataset):
         'vh_vv(t-3)',
         'lat',
         'lon',
-    ]
+    )
 
     def __init__(
         self,
         root: Path = 'data',
-        input_features: list[str] = all_variable_names,
+        input_features: Iterable[str] = all_variable_names,
         transforms: Callable[[dict[str, Any]], dict[str, Any]] | None = None,
         download: bool = False,
     ) -> None:
@@ -273,7 +273,7 @@ class WesternUSALiveFuelMoisture(NonGeoDataset):
                 data_rows.append(data_dict)
 
         df = pd.DataFrame(data_rows)
-        df = df[self.input_features + [self.label_name]]
+        df = df[[*self.input_features, self.label_name]]
         return df
 
     def _verify(self) -> None:

torchgeo/datasets/xview.py

@@ -6,6 +6,7 @@
 import glob
 import os
 from collections.abc import Callable
+from typing import ClassVar
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -54,7 +55,7 @@ class XView2(NonGeoDataset):
     .. versionadded:: 0.2
     """
 
-    metadata = {
+    metadata: ClassVar[dict[str, dict[str, str]]] = {
         'train': {
             'filename': 'train_images_labels_targets.tar.gz',
             'md5': 'a20ebbfb7eb3452785b63ad02ffd1e16',
@@ -66,8 +67,8 @@ class XView2(NonGeoDataset):
             'directory': 'test',
         },
     }
-    classes = ['background', 'no-damage', 'minor-damage', 'major-damage', 'destroyed']
+    classes = ('background', 'no-damage', 'minor-damage', 'major-damage', 'destroyed')
-    colormap = ['green', 'blue', 'orange', 'red']
+    colormap = ('green', 'blue', 'orange', 'red')
 
     def __init__(
         self,
@@ -242,10 +243,16 @@ class XView2(NonGeoDataset):
         """
         ncols = 2
         image1 = draw_semantic_segmentation_masks(
-            sample['image'][0], sample['mask'][0], alpha=alpha, colors=self.colormap
+            sample['image'][0],
+            sample['mask'][0],
+            alpha=alpha,
+            colors=list(self.colormap),
         )
         image2 = draw_semantic_segmentation_masks(
-            sample['image'][1], sample['mask'][1], alpha=alpha, colors=self.colormap
+            sample['image'][1],
+            sample['mask'][1],
+            alpha=alpha,
+            colors=list(self.colormap),
         )
         if 'prediction' in sample:  # NOTE: this assumes predictions are made for post
             ncols += 1
@@ -253,7 +260,7 @@ class XView2(NonGeoDataset):
                 sample['image'][1],
                 sample['prediction'],
                 alpha=alpha,
-                colors=self.colormap,
+                colors=list(self.colormap),
             )
 
         fig, axs = plt.subplots(ncols=ncols, figsize=(ncols * 10, 10))

torchgeo/datasets/zuericrop.py

@@ -52,15 +52,15 @@ class ZueriCrop(NonGeoDataset):
        * `h5py <https://pypi.org/project/h5py/>`_ to load the dataset
     """
 
-    urls = [
+    urls = (
         'https://polybox.ethz.ch/index.php/s/uXfdr2AcXE3QNB6/download',
-        'https://raw.githubusercontent.com/0zgur0/multi-stage-convSTAR-network/fa92b5b3cb77f5171c5c3be740cd6e6395cc29b6/labels.csv',  # noqa: E501
+        'https://raw.githubusercontent.com/0zgur0/multi-stage-convSTAR-network/fa92b5b3cb77f5171c5c3be740cd6e6395cc29b6/labels.csv',
-    ]
+    )
-    md5s = ['1635231df67f3d25f4f1e62c98e221a4', '5118398c7a5bbc246f5f6bb35d8d529b']
+    md5s = ('1635231df67f3d25f4f1e62c98e221a4', '5118398c7a5bbc246f5f6bb35d8d529b')
-    filenames = ['ZueriCrop.hdf5', 'labels.csv']
+    filenames = ('ZueriCrop.hdf5', 'labels.csv')
 
     band_names = ('NIR', 'B03', 'B02', 'B04', 'B05', 'B06', 'B07', 'B11', 'B12')
-    rgb_bands = ['B04', 'B03', 'B02']
+    rgb_bands = ('B04', 'B03', 'B02')
 
     def __init__(
         self,

torchgeo/main.py

@@ -8,7 +8,7 @@ import os
 from lightning.pytorch.cli import ArgsType, LightningCLI
 
 # Allows classes to be referenced using only the class name
-import torchgeo.datamodules  # noqa: F401
+import torchgeo.datamodules
 import torchgeo.trainers  # noqa: F401
 from torchgeo.datamodules import BaseDataModule
 from torchgeo.trainers import BaseTask

torchgeo/models/api.py

@@ -8,7 +8,7 @@ See the following references for design details:
 * https://pytorch.org/blog/easily-list-and-initialize-models-with-new-apis-in-torchvision/
 * https://pytorch.org/vision/stable/models.html
 * https://github.com/pytorch/vision/blob/main/torchvision/models/_api.py
-"""  # noqa: E501
+"""
 
 from collections.abc import Callable
 from typing import Any

torchgeo/models/dofa.py

@@ -384,7 +384,7 @@ class DOFABase16_Weights(WeightsEnum):  # type: ignore[misc]
     """
 
     DOFA_MAE = Weights(
-        url='https://hf.co/torchgeo/dofa/resolve/ade8745c5ec6eddfe15d8c03421e8cb8f21e66ff/dofa_base_patch16_224-7cc0f413.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/dofa/resolve/ade8745c5ec6eddfe15d8c03421e8cb8f21e66ff/dofa_base_patch16_224-7cc0f413.pth',
         transforms=_dofa_transforms,
         meta={
             'dataset': 'SatlasPretrain, Five-Billion-Pixels, HySpecNet-11k',
@@ -403,7 +403,7 @@ class DOFALarge16_Weights(WeightsEnum):  # type: ignore[misc]
     """
 
     DOFA_MAE = Weights(
-        url='https://hf.co/torchgeo/dofa/resolve/ade8745c5ec6eddfe15d8c03421e8cb8f21e66ff/dofa_large_patch16_224-fbd47fa9.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/dofa/resolve/ade8745c5ec6eddfe15d8c03421e8cb8f21e66ff/dofa_large_patch16_224-fbd47fa9.pth',
         transforms=_dofa_transforms,
         meta={
             'dataset': 'SatlasPretrain, Five-Billion-Pixels, HySpecNet-11k',

torchgeo/models/rcf.py

@@ -140,7 +140,7 @@ class RCF(Module):
             a numpy array of size (N, C, H, W) containing the normalized patches
 
         .. versionadded:: 0.5
-        """  # noqa: E501
+        """
         n_patches = patches.shape[0]
         orig_shape = patches.shape
         patches = patches.reshape(patches.shape[0], -1)

torchgeo/models/resnet.py

@@ -11,8 +11,8 @@ import torch
 from timm.models import ResNet
 from torchvision.models._api import Weights, WeightsEnum
 
-# https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/linear_BE_moco.py#L167  # noqa: E501
+# https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/linear_BE_moco.py#L167
-# https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/datasets/EuroSat/eurosat_dataset.py#L97  # noqa: E501
+# https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/datasets/EuroSat/eurosat_dataset.py#L97
 # Normalization either by 10K or channel-wise with band statistics
 _zhu_xlab_transforms = K.AugmentationSequential(
     K.Resize(256),
@@ -22,7 +22,7 @@ _zhu_xlab_transforms = K.AugmentationSequential(
 )
 
 # Normalization only available for RGB dataset, defined here:
-# https://github.com/ServiceNow/seasonal-contrast/blob/8285173ec205b64bc3e53b880344dd6c3f79fa7a/datasets/seco_dataset.py  # noqa: E501
+# https://github.com/ServiceNow/seasonal-contrast/blob/8285173ec205b64bc3e53b880344dd6c3f79fa7a/datasets/seco_dataset.py
 _min = torch.tensor([3, 2, 0])
 _max = torch.tensor([88, 103, 129])
 _mean = torch.tensor([0.485, 0.456, 0.406])
@@ -37,7 +37,7 @@ _seco_transforms = K.AugmentationSequential(
 )
 
 # Normalization only available for RGB dataset, defined here:
-# https://github.com/sustainlab-group/geography-aware-ssl/blob/main/moco_fmow/main_moco_geo%2Btp.py#L287  # noqa: E501
+# https://github.com/sustainlab-group/geography-aware-ssl/blob/main/moco_fmow/main_moco_geo%2Btp.py#L287
 _mean = torch.tensor([0.485, 0.456, 0.406])
 _std = torch.tensor([0.229, 0.224, 0.225])
 _gassl_transforms = K.AugmentationSequential(
@@ -47,7 +47,7 @@ _gassl_transforms = K.AugmentationSequential(
     data_keys=None,
 )
 
-# https://github.com/microsoft/torchgeo/blob/8b53304d42c269f9001cb4e861a126dc4b462606/torchgeo/datamodules/ssl4eo_benchmark.py#L43  # noqa: E501
+# https://github.com/microsoft/torchgeo/blob/8b53304d42c269f9001cb4e861a126dc4b462606/torchgeo/datamodules/ssl4eo_benchmark.py#L43
 _ssl4eo_l_transforms = K.AugmentationSequential(
     K.Normalize(mean=torch.tensor(0), std=torch.tensor(255)),
     K.CenterCrop((224, 224)),
@@ -70,7 +70,7 @@ class ResNet18_Weights(WeightsEnum):  # type: ignore[misc]
     """
 
     LANDSAT_TM_TOA_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_tm_toa_moco-1c691b4f.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_tm_toa_moco-1c691b4f.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -83,7 +83,7 @@ class ResNet18_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_TM_TOA_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_tm_toa_simclr-d2d38ace.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_tm_toa_simclr-d2d38ace.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -96,7 +96,7 @@ class ResNet18_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_ETM_TOA_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_etm_toa_moco-bb88689c.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_etm_toa_moco-bb88689c.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -109,7 +109,7 @@ class ResNet18_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_ETM_TOA_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_etm_toa_simclr-4d813f79.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_etm_toa_simclr-4d813f79.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -122,7 +122,7 @@ class ResNet18_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_ETM_SR_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_etm_sr_moco-4f078acd.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_etm_sr_moco-4f078acd.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -135,7 +135,7 @@ class ResNet18_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_ETM_SR_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_etm_sr_simclr-8e8543b4.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_etm_sr_simclr-8e8543b4.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -148,7 +148,7 @@ class ResNet18_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_OLI_TIRS_TOA_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_oli_tirs_toa_moco-a3002f51.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_oli_tirs_toa_moco-a3002f51.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -161,7 +161,7 @@ class ResNet18_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_OLI_TIRS_TOA_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_oli_tirs_toa_simclr-b0635cc6.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_oli_tirs_toa_simclr-b0635cc6.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -174,7 +174,7 @@ class ResNet18_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_OLI_SR_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_oli_sr_moco-660e82ed.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_oli_sr_moco-660e82ed.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -187,7 +187,7 @@ class ResNet18_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_OLI_SR_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_oli_sr_simclr-7bced5be.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet18_landsat_oli_sr_simclr-7bced5be.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -200,7 +200,7 @@ class ResNet18_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     SENTINEL2_ALL_MOCO = Weights(
-        url='https://hf.co/torchgeo/resnet18_sentinel2_all_moco/resolve/5b8cddc9a14f3844350b7f40b85bcd32aed75918/resnet18_sentinel2_all_moco-59bfdff9.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/resnet18_sentinel2_all_moco/resolve/5b8cddc9a14f3844350b7f40b85bcd32aed75918/resnet18_sentinel2_all_moco-59bfdff9.pth',
         transforms=_zhu_xlab_transforms,
         meta={
             'dataset': 'SSL4EO-S12',
@@ -213,7 +213,7 @@ class ResNet18_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     SENTINEL2_RGB_MOCO = Weights(
-        url='https://hf.co/torchgeo/resnet18_sentinel2_rgb_moco/resolve/e1c032e7785fd0625224cdb6699aa138bb304eec/resnet18_sentinel2_rgb_moco-e3a335e3.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/resnet18_sentinel2_rgb_moco/resolve/e1c032e7785fd0625224cdb6699aa138bb304eec/resnet18_sentinel2_rgb_moco-e3a335e3.pth',
         transforms=_zhu_xlab_transforms,
         meta={
             'dataset': 'SSL4EO-S12',
@@ -226,7 +226,7 @@ class ResNet18_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     SENTINEL2_RGB_SECO = Weights(
-        url='https://hf.co/torchgeo/resnet18_sentinel2_rgb_seco/resolve/f8dcee692cf7142163b55a5c197d981fe0e717a0/resnet18_sentinel2_rgb_seco-cefca942.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/resnet18_sentinel2_rgb_seco/resolve/f8dcee692cf7142163b55a5c197d981fe0e717a0/resnet18_sentinel2_rgb_seco-cefca942.pth',
         transforms=_seco_transforms,
         meta={
             'dataset': 'SeCo Dataset',
@@ -249,7 +249,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     """
 
     FMOW_RGB_GASSL = Weights(
-        url='https://hf.co/torchgeo/resnet50_fmow_rgb_gassl/resolve/fe8a91026cf9104f1e884316b8e8772d7af9052c/resnet50_fmow_rgb_gassl-da43d987.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/resnet50_fmow_rgb_gassl/resolve/fe8a91026cf9104f1e884316b8e8772d7af9052c/resnet50_fmow_rgb_gassl-da43d987.pth',
         transforms=_gassl_transforms,
         meta={
             'dataset': 'fMoW Dataset',
@@ -262,7 +262,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_TM_TOA_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_tm_toa_moco-ba1ce753.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_tm_toa_moco-ba1ce753.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -275,7 +275,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_TM_TOA_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_tm_toa_simclr-a1c93432.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_tm_toa_simclr-a1c93432.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -288,7 +288,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_ETM_TOA_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_etm_toa_moco-e9a84d5a.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_etm_toa_moco-e9a84d5a.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -301,7 +301,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_ETM_TOA_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_etm_toa_simclr-70b5575f.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_etm_toa_simclr-70b5575f.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -314,7 +314,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_ETM_SR_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_etm_sr_moco-1266cde3.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_etm_sr_moco-1266cde3.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -327,7 +327,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_ETM_SR_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_etm_sr_simclr-e5d185d7.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_etm_sr_simclr-e5d185d7.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -340,7 +340,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_OLI_TIRS_TOA_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_oli_tirs_toa_moco-de7f5e0f.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_oli_tirs_toa_moco-de7f5e0f.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -353,7 +353,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_OLI_TIRS_TOA_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_oli_tirs_toa_simclr-030cebfe.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_oli_tirs_toa_simclr-030cebfe.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -366,7 +366,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_OLI_SR_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_oli_sr_moco-ff580dad.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_oli_sr_moco-ff580dad.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -379,7 +379,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_OLI_SR_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_oli_sr_simclr-94f78913.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/resnet50_landsat_oli_sr_simclr-94f78913.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -392,7 +392,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     SENTINEL1_ALL_MOCO = Weights(
-        url='https://hf.co/torchgeo/resnet50_sentinel1_all_moco/resolve/e79862c667853c10a709bdd77ea8ffbad0e0f1cf/resnet50_sentinel1_all_moco-906e4356.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/resnet50_sentinel1_all_moco/resolve/e79862c667853c10a709bdd77ea8ffbad0e0f1cf/resnet50_sentinel1_all_moco-906e4356.pth',
         transforms=_zhu_xlab_transforms,
         meta={
             'dataset': 'SSL4EO-S12',
@@ -405,7 +405,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     SENTINEL2_ALL_DINO = Weights(
-        url='https://hf.co/torchgeo/resnet50_sentinel2_all_dino/resolve/d7f14bf5530d70ac69d763e58e77e44dbecfec7c/resnet50_sentinel2_all_dino-d6c330e9.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/resnet50_sentinel2_all_dino/resolve/d7f14bf5530d70ac69d763e58e77e44dbecfec7c/resnet50_sentinel2_all_dino-d6c330e9.pth',
         transforms=_zhu_xlab_transforms,
         meta={
             'dataset': 'SSL4EO-S12',
@@ -418,7 +418,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     SENTINEL2_ALL_MOCO = Weights(
-        url='https://hf.co/torchgeo/resnet50_sentinel2_all_moco/resolve/da4f3c9dbe09272eb902f3b37f46635fa4726879/resnet50_sentinel2_all_moco-df8b932e.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/resnet50_sentinel2_all_moco/resolve/da4f3c9dbe09272eb902f3b37f46635fa4726879/resnet50_sentinel2_all_moco-df8b932e.pth',
         transforms=_zhu_xlab_transforms,
         meta={
             'dataset': 'SSL4EO-S12',
@@ -431,7 +431,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     SENTINEL2_RGB_MOCO = Weights(
-        url='https://hf.co/torchgeo/resnet50_sentinel2_rgb_moco/resolve/efd9723b59a88e9dc1420dc1e96afb25b0630a3c/resnet50_sentinel2_rgb_moco-2b57ba8b.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/resnet50_sentinel2_rgb_moco/resolve/efd9723b59a88e9dc1420dc1e96afb25b0630a3c/resnet50_sentinel2_rgb_moco-2b57ba8b.pth',
         transforms=_zhu_xlab_transforms,
         meta={
             'dataset': 'SSL4EO-S12',
@@ -444,7 +444,7 @@ class ResNet50_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     SENTINEL2_RGB_SECO = Weights(
-        url='https://hf.co/torchgeo/resnet50_sentinel2_rgb_seco/resolve/fbd07b02a8edb8fc1035f7957160deed4321c145/resnet50_sentinel2_rgb_seco-018bf397.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/resnet50_sentinel2_rgb_seco/resolve/fbd07b02a8edb8fc1035f7957160deed4321c145/resnet50_sentinel2_rgb_seco-018bf397.pth',
         transforms=_seco_transforms,
         meta={
             'dataset': 'SeCo Dataset',

torchgeo/models/swin.py

@@ -12,20 +12,20 @@ from kornia.contrib import Lambda
 from torchvision.models import SwinTransformer
 from torchvision.models._api import Weights, WeightsEnum
 
-# https://github.com/allenai/satlas/blob/bcaa968da5395f675d067613e02613a344e81415/satlas/cmd/model/train.py#L42 # noqa: E501
+# https://github.com/allenai/satlas/blob/bcaa968da5395f675d067613e02613a344e81415/satlas/cmd/model/train.py#L42
 # Satlas uses the TCI product for Sentinel-2 RGB, which is in the range (0, 255).
-# See details:  https://github.com/allenai/satlas/blob/main/Normalization.md#sentinel-2-images.  # noqa: E501
+# See details:  https://github.com/allenai/satlas/blob/main/Normalization.md#sentinel-2-images.
-# Satlas Sentinel-1 and RGB Sentinel-2 and NAIP imagery is uint8 and is normalized to (0, 1) by dividing by 255. # noqa: E501
+# Satlas Sentinel-1 and RGB Sentinel-2 and NAIP imagery is uint8 and is normalized to (0, 1) by dividing by 255.
 _satlas_transforms = K.AugmentationSequential(
     K.Normalize(mean=torch.tensor(0), std=torch.tensor(255)), data_keys=None
 )
 
 # Satlas uses the TCI product for Sentinel-2 RGB, which is in the range (0, 255).
-# See details:  https://github.com/allenai/satlas/blob/main/Normalization.md#sentinel-2-images.  # noqa: E501
+# See details:  https://github.com/allenai/satlas/blob/main/Normalization.md#sentinel-2-images.
-# Satlas Sentinel-2 multispectral imagery has first 3 bands divided by 255 and the following 6 bands by 8160, both clipped to (0, 1). # noqa: E501
+# Satlas Sentinel-2 multispectral imagery has first 3 bands divided by 255 and the following 6 bands by 8160, both clipped to (0, 1).
 _std = torch.tensor(
     [255.0, 255.0, 255.0, 8160.0, 8160.0, 8160.0, 8160.0, 8160.0, 8160.0]
-)  # noqa: E501
+)
 _mean = torch.zeros_like(_std)
 _sentinel2_ms_satlas_transforms = K.AugmentationSequential(
     K.Normalize(mean=_mean, std=_std),
@@ -33,7 +33,7 @@ _sentinel2_ms_satlas_transforms = K.AugmentationSequential(
     data_keys=None,
 )
 
-# Satlas Landsat imagery is 16-bit, normalized by clipping some pixel N with (N-4000)/16320 to (0, 1). # noqa: E501
+# Satlas Landsat imagery is 16-bit, normalized by clipping some pixel N with (N-4000)/16320 to (0, 1).
 _landsat_satlas_transforms = K.AugmentationSequential(
     K.Normalize(mean=torch.tensor(4000), std=torch.tensor(16320)),
     K.ImageSequential(Lambda(lambda x: torch.clamp(x, min=0.0, max=1.0))),
@@ -56,7 +56,7 @@ class Swin_V2_B_Weights(WeightsEnum):  # type: ignore[misc]
     """
 
     NAIP_RGB_SI_SATLAS = Weights(
-        url='https://hf.co/allenai/satlas-pretrain/resolve/daa578a4be36573d9791bf51dcd0420b8dc75732/aerial_swinb_si.pth',  # noqa: E501
+        url='https://hf.co/allenai/satlas-pretrain/resolve/daa578a4be36573d9791bf51dcd0420b8dc75732/aerial_swinb_si.pth',
         transforms=_satlas_transforms,
         meta={
             'dataset': 'Satlas',
@@ -68,7 +68,7 @@ class Swin_V2_B_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     SENTINEL2_RGB_SI_SATLAS = Weights(
-        url='https://hf.co/allenai/satlas-pretrain/resolve/daa578a4be36573d9791bf51dcd0420b8dc75732/sentinel2_swinb_si_rgb.pth',  # noqa: E501
+        url='https://hf.co/allenai/satlas-pretrain/resolve/daa578a4be36573d9791bf51dcd0420b8dc75732/sentinel2_swinb_si_rgb.pth',
         transforms=_satlas_transforms,
         meta={
             'dataset': 'Satlas',
@@ -80,7 +80,7 @@ class Swin_V2_B_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     SENTINEL2_MS_SI_SATLAS = Weights(
-        url='https://hf.co/allenai/satlas-pretrain/resolve/daa578a4be36573d9791bf51dcd0420b8dc75732/sentinel2_swinb_si_ms.pth',  # noqa: E501
+        url='https://hf.co/allenai/satlas-pretrain/resolve/daa578a4be36573d9791bf51dcd0420b8dc75732/sentinel2_swinb_si_ms.pth',
         transforms=_sentinel2_ms_satlas_transforms,
         meta={
             'dataset': 'Satlas',
@@ -93,7 +93,7 @@ class Swin_V2_B_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     SENTINEL1_SI_SATLAS = Weights(
-        url='https://hf.co/allenai/satlas-pretrain/resolve/daa578a4be36573d9791bf51dcd0420b8dc75732/sentinel1_swinb_si.pth',  # noqa: E501
+        url='https://hf.co/allenai/satlas-pretrain/resolve/daa578a4be36573d9791bf51dcd0420b8dc75732/sentinel1_swinb_si.pth',
         transforms=_satlas_transforms,
         meta={
             'dataset': 'Satlas',
@@ -106,7 +106,7 @@ class Swin_V2_B_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_SI_SATLAS = Weights(
-        url='https://hf.co/allenai/satlas-pretrain/resolve/daa578a4be36573d9791bf51dcd0420b8dc75732/landsat_swinb_si.pth',  # noqa: E501
+        url='https://hf.co/allenai/satlas-pretrain/resolve/daa578a4be36573d9791bf51dcd0420b8dc75732/landsat_swinb_si.pth',
         transforms=_landsat_satlas_transforms,
         meta={
             'dataset': 'Satlas',
@@ -126,7 +126,7 @@ class Swin_V2_B_Weights(WeightsEnum):  # type: ignore[misc]
                 'B09',
                 'B10',
                 'B11',
-            ],  # noqa: E501
+            ],
         },
     )
 

torchgeo/models/vit.py

@@ -11,8 +11,8 @@ import torch
 from timm.models.vision_transformer import VisionTransformer
 from torchvision.models._api import Weights, WeightsEnum
 
-# https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/linear_BE_moco.py#L167 # noqa: E501
+# https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/linear_BE_moco.py#L167
-# https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/datasets/EuroSat/eurosat_dataset.py#L97 # noqa: E501
+# https://github.com/zhu-xlab/SSL4EO-S12/blob/d2868adfada65e40910bfcedfc49bc3b20df2248/src/benchmark/transfer_classification/datasets/EuroSat/eurosat_dataset.py#L97
 # Normalization either by 10K or channel-wise with band statistics
 _zhu_xlab_transforms = K.AugmentationSequential(
     K.Resize(256),
@@ -21,7 +21,7 @@ _zhu_xlab_transforms = K.AugmentationSequential(
     data_keys=None,
 )
 
-# https://github.com/microsoft/torchgeo/blob/8b53304d42c269f9001cb4e861a126dc4b462606/torchgeo/datamodules/ssl4eo_benchmark.py#L43 # noqa: E501
+# https://github.com/microsoft/torchgeo/blob/8b53304d42c269f9001cb4e861a126dc4b462606/torchgeo/datamodules/ssl4eo_benchmark.py#L43
 _ssl4eo_l_transforms = K.AugmentationSequential(
     K.Normalize(mean=torch.tensor(0), std=torch.tensor(255)),
     K.CenterCrop((224, 224)),
@@ -44,7 +44,7 @@ class ViTSmall16_Weights(WeightsEnum):  # type: ignore[misc]
     """
 
     LANDSAT_TM_TOA_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_tm_toa_moco-a1c967d8.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_tm_toa_moco-a1c967d8.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -57,7 +57,7 @@ class ViTSmall16_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_TM_TOA_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_tm_toa_simclr-7c2d9799.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_tm_toa_simclr-7c2d9799.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -70,7 +70,7 @@ class ViTSmall16_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_ETM_TOA_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_etm_toa_moco-26d19bcf.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_etm_toa_moco-26d19bcf.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -83,7 +83,7 @@ class ViTSmall16_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_ETM_TOA_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_etm_toa_simclr-34fb12cb.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_etm_toa_simclr-34fb12cb.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -96,7 +96,7 @@ class ViTSmall16_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_ETM_SR_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_etm_sr_moco-eaa4674e.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_etm_sr_moco-eaa4674e.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -109,7 +109,7 @@ class ViTSmall16_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_ETM_SR_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_etm_sr_simclr-a14c466a.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_etm_sr_simclr-a14c466a.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -122,7 +122,7 @@ class ViTSmall16_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_OLI_TIRS_TOA_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_oli_tirs_toa_moco-c7c2cceb.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_oli_tirs_toa_moco-c7c2cceb.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -135,7 +135,7 @@ class ViTSmall16_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_OLI_TIRS_TOA_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_oli_tirs_toa_simclr-ad43e9a4.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_oli_tirs_toa_simclr-ad43e9a4.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -148,7 +148,7 @@ class ViTSmall16_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_OLI_SR_MOCO = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_oli_sr_moco-c9b8898d.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_oli_sr_moco-c9b8898d.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -161,7 +161,7 @@ class ViTSmall16_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     LANDSAT_OLI_SR_SIMCLR = Weights(
-        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_oli_sr_simclr-4e8f6102.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/ssl4eo_landsat/resolve/1c88bb51b6e17a21dde5230738fa38b74bd74f76/vits16_landsat_oli_sr_simclr-4e8f6102.pth',
         transforms=_ssl4eo_l_transforms,
         meta={
             'dataset': 'SSL4EO-L',
@@ -174,7 +174,7 @@ class ViTSmall16_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     SENTINEL2_ALL_DINO = Weights(
-        url='https://hf.co/torchgeo/vit_small_patch16_224_sentinel2_all_dino/resolve/5b41dd418a79de47ac9f5be3e035405a83818a62/vit_small_patch16_224_sentinel2_all_dino-36bcc127.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/vit_small_patch16_224_sentinel2_all_dino/resolve/5b41dd418a79de47ac9f5be3e035405a83818a62/vit_small_patch16_224_sentinel2_all_dino-36bcc127.pth',
         transforms=_zhu_xlab_transforms,
         meta={
             'dataset': 'SSL4EO-S12',
@@ -187,7 +187,7 @@ class ViTSmall16_Weights(WeightsEnum):  # type: ignore[misc]
     )
 
     SENTINEL2_ALL_MOCO = Weights(
-        url='https://hf.co/torchgeo/vit_small_patch16_224_sentinel2_all_moco/resolve/1cb683f6c14739634cdfaaceb076529adf898c74/vit_small_patch16_224_sentinel2_all_moco-67c9032d.pth',  # noqa: E501
+        url='https://hf.co/torchgeo/vit_small_patch16_224_sentinel2_all_moco/resolve/1cb683f6c14739634cdfaaceb076529adf898c74/vit_small_patch16_224_sentinel2_all_moco-67c9032d.pth',
         transforms=_zhu_xlab_transforms,
         meta={
             'dataset': 'SSL4EO-S12',

torchgeo/transforms/transforms.py

@@ -53,7 +53,7 @@ class AugmentationSequential(Module):
             else:
                 keys.append(key)
 
-        self.augs = K.AugmentationSequential(*args, data_keys=keys, **kwargs)  # type: ignore[arg-type] # noqa: E501
+        self.augs = K.AugmentationSequential(*args, data_keys=keys, **kwargs)  # type: ignore[arg-type]
 
     def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
         """Perform augmentations and update data dict.