diff --git a/tests/conf/ssl4eo_l_benchmark_cdl.yaml b/tests/conf/ssl4eo_l_benchmark_cdl.yaml
new file mode 100644
index 00000000000..a6390d9e392
--- /dev/null
+++ b/tests/conf/ssl4eo_l_benchmark_cdl.yaml
@@ -0,0 +1,20 @@
+module:
+  _target_: torchgeo.trainers.SemanticSegmentationTask
+  loss: "ce"
+  model: "unet"
+  backbone: "resnet18"
+  weights: null
+  learning_rate: 1e-3
+  learning_rate_schedule_patience: 6
+  in_channels: 7
+  num_classes: 134
+  num_filters: 1
+  ignore_index: 0
+
+datamodule:
+  _target_: torchgeo.datamodules.SSL4EOLBenchmarkDataModule
+  root: "tests/data/ssl4eo_benchmark_landsat"
+  input_sensor: "tm_toa"
+  mask_product: "cdl"
+  batch_size: 2
+  num_workers: 0
diff --git a/tests/conf/ssl4eo_l_benchmark_nlcd.yaml b/tests/conf/ssl4eo_l_benchmark_nlcd.yaml
new file mode 100644
index 00000000000..94cc899b535
--- /dev/null
+++ b/tests/conf/ssl4eo_l_benchmark_nlcd.yaml
@@ -0,0 +1,20 @@
+module:
+  _target_: torchgeo.trainers.SemanticSegmentationTask
+  loss: "ce"
+  model: "unet"
+  backbone: "resnet18"
+  weights: null
+  learning_rate: 1e-3
+  learning_rate_schedule_patience: 6
+  in_channels: 6
+  num_classes: 17
+  num_filters: 1
+  ignore_index: 0
+
+datamodule:
+  _target_: torchgeo.datamodules.SSL4EOLBenchmarkDataModule
+  root: "tests/data/ssl4eo_benchmark_landsat"
+  input_sensor: "etm_sr"
+  mask_product: "nlcd"
+  batch_size: 2
+  num_workers: 0
diff --git a/tests/data/ssl4eo_benchmark_landsat/data.py b/tests/data/ssl4eo_benchmark_landsat/data.py
new file mode 100644
index 00000000000..646208943b4
--- /dev/null
+++ b/tests/data/ssl4eo_benchmark_landsat/data.py
@@ -0,0 +1,218 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import hashlib
+import os
+import shutil
+from typing import Union
+
+import numpy as np
+import rasterio
+from rasterio import Affine
+from rasterio.crs import CRS
+
+SIZE = 264
+
+np.random.seed(0)
+
+FILENAME_HIERARCHY = Union[dict[str, "FILENAME_HIERARCHY"], list[str]]
+
+filenames: FILENAME_HIERARCHY = {
+    "tm_toa": {
+        "0000001": {"LT05_172030_20010526": ["all_bands.tif"]},
+        "0000002": {"LT05_223084_20010413": ["all_bands.tif"]},
+        "0000003": {"LT05_172034_20020902": ["all_bands.tif"]},
+        "0000004": {"LT05_172034_20020903": ["all_bands.tif"]},
+        "0000005": {"LT05_172034_20020904": ["all_bands.tif"]},
+        "0000006": {"LT05_172034_20020905": ["all_bands.tif"]},
+        "0000007": {"LT05_172034_20020906": ["all_bands.tif"]},
+        "0000008": {"LT05_172034_20020907": ["all_bands.tif"]},
+        "0000009": {"LT05_172034_20020908": ["all_bands.tif"]},
+        "0000010": {"LT05_172034_20020909": ["all_bands.tif"]},
+    },
+    "etm_sr": {
+        "0000001": {"LE07_172030_20010526": ["all_bands.tif"]},
+        "0000002": {"LE07_223084_20010413": ["all_bands.tif"]},
+        "0000003": {"LE07_172034_20020902": ["all_bands.tif"]},
+        "0000004": {"LE07_172034_20020903": ["all_bands.tif"]},
+        "0000005": {"LE07_172034_20020904": ["all_bands.tif"]},
+        "0000006": {"LE07_172034_20020905": ["all_bands.tif"]},
+        "0000007": {"LE07_172034_20020906": ["all_bands.tif"]},
+        "0000008": {"LE07_172034_20020907": ["all_bands.tif"]},
+        "0000009": {"LE07_172034_20020908": ["all_bands.tif"]},
+        "0000010": {"LE07_172034_20020909": ["all_bands.tif"]},
+    },
+    "etm_toa": {
+        "0000001": {"LE07_172030_20010526": ["all_bands.tif"]},
+        "0000002": {"LE07_223084_20010413": ["all_bands.tif"]},
+        "0000003": {"LE07_172034_20020902": ["all_bands.tif"]},
+        "0000004": {"LE07_172034_20020903": ["all_bands.tif"]},
+        "0000005": {"LE07_172034_20020904": ["all_bands.tif"]},
+        "0000006": {"LE07_172034_20020905": ["all_bands.tif"]},
+        "0000007": {"LE07_172034_20020906": ["all_bands.tif"]},
+        "0000008": {"LE07_172034_20020907": ["all_bands.tif"]},
+        "0000009": {"LE07_172034_20020908": ["all_bands.tif"]},
+        "0000010": {"LE07_172034_20020909": ["all_bands.tif"]},
+    },
+    "oli_tirs_toa": {
+        "0000001": {"LC08_172030_20010526": ["all_bands.tif"]},
+        "0000002": {"LC08_223084_20010413": ["all_bands.tif"]},
+        "0000003": {"LC08_172034_20020902": ["all_bands.tif"]},
+        "0000004": {"LC08_172034_20020903": ["all_bands.tif"]},
+        "0000005": {"LC08_172034_20020904": ["all_bands.tif"]},
+        "0000006": {"LC08_172034_20020905": ["all_bands.tif"]},
+        "0000007": {"LC08_172034_20020906": ["all_bands.tif"]},
+        "0000008": {"LC08_172034_20020907": ["all_bands.tif"]},
+        "0000009": {"LC08_172034_20020908": ["all_bands.tif"]},
+        "0000010": {"LC08_172034_20020909": ["all_bands.tif"]},
+    },
+    "oli_sr": {
+        "0000001": {"LC08_172030_20010526": ["all_bands.tif"]},
+        "0000002": {"LC08_223084_20010413": ["all_bands.tif"]},
+        "0000003": {"LC08_172034_20020902": ["all_bands.tif"]},
+        "0000004": {"LC08_172034_20020903": ["all_bands.tif"]},
+        "0000005": {"LC08_172034_20020904": ["all_bands.tif"]},
+        "0000006": {"LC08_172034_20020905": ["all_bands.tif"]},
+        "0000007": {"LC08_172034_20020906": ["all_bands.tif"]},
+        "0000008": {"LC08_172034_20020907": ["all_bands.tif"]},
+        "0000009": {"LC08_172034_20020908": ["all_bands.tif"]},
+        "0000010": {"LC08_172034_20020909": ["all_bands.tif"]},
+    },
+}
+
+num_bands = {"tm_toa": 7, "etm_sr": 6, "etm_toa": 9, "oli_tirs_toa": 11, "oli_sr": 7}
+years = {"tm": 2011, "etm": 2019, "oli": 2019}
+
+
+def create_image(path: str) -> None:
+    profile = {
+        "driver": "GTiff",
+        "dtype": "uint8",
+        "nodata": None,
+        "width": SIZE,
+        "height": SIZE,
+        "count": num_bands["_".join(path.split(os.sep)[1].split("_")[2:][:-1])],
+        "crs": CRS.from_epsg(4326),
+        "transform": Affine(
+            0.00037672803497508636,
+            0.0,
+            -109.07063613660262,
+            0.0,
+            -0.0002554026278261721,
+            47.49838726154881,
+        ),
+        "blockysize": 1,
+        "tiled": False,
+        "compress": "lzw",
+        "interleave": "pixel",
+    }
+
+    Z = np.random.randint(low=0, high=255, size=(SIZE, SIZE))
+
+    with rasterio.open(path, "w", **profile) as src:
+        for i in src.indexes:
+            src.write(Z, i)
+
+
+def create_mask(path: str) -> None:
+    profile = {
+        "driver": "GTiff",
+        "dtype": "uint8",
+        "nodata": None,
+        "width": SIZE,
+        "height": SIZE,
+        "count": 1,
+        "crs": CRS.from_epsg(4326),
+        "transform": Affine(
+            0.00037672803497508636,
+            0.0,
+            -109.07063613660262,
+            0.0,
+            -0.0002554026278261721,
+            47.49838726154881,
+        ),
+        "blockysize": 1,
+        "tiled": False,
+        "compress": "lzw",
+        "interleave": "band",
+    }
+
+    Z = np.random.randint(low=0, high=10, size=(1, SIZE, SIZE))
+
+    with rasterio.open(path, "w", **profile) as src:
+        src.write(Z)
+
+
+def create_img_directory(directory: str, hierarchy: FILENAME_HIERARCHY) -> None:
+    if isinstance(hierarchy, dict):
+        # Recursive case
+        for key, value in hierarchy.items():
+            if any([x in key for x in filenames.keys()]):
+                key = f"ssl4eo_l_{key}_benchmark"
+            path = os.path.join(directory, key)
+            os.makedirs(path, exist_ok=True)
+            create_img_directory(path, value)
+    else:
+        # Base case
+        for value in hierarchy:
+            path = os.path.join(directory, value)
+            create_image(path)
+
+
+def create_mask_directory(
+    directory: str, hierarchy: FILENAME_HIERARCHY, mask_product: str
+) -> None:
+    if isinstance(hierarchy, dict):
+        # Recursive case
+        for key, value in hierarchy.items():
+            path = os.path.join(directory, key)
+            os.makedirs(path, exist_ok=True)
+            create_mask_directory(path, value, mask_product)
+    else:
+        # Base case
+        for value in hierarchy:
+            path = os.path.join(directory, value)
+            year = years[path.split(os.sep)[1].split("_")[2]]
+            create_mask(path.replace("all_bands", f"{mask_product}_{year}"))
+
+
+def create_tarballs(directories) -> None:
+    for directory in directories:
+        # Create tarballs
+        shutil.make_archive(directory, "gztar", ".", directory)
+
+        # Compute checksums
+        with open(f"{directory}.tar.gz", "rb") as f:
+            md5 = hashlib.md5(f.read()).hexdigest()
+            print(directory, md5)
+
+
+if __name__ == "__main__":
+    # image directories
+    create_img_directory(".", filenames)
+    directories = filenames.keys()
+    directories = [f"ssl4eo_l_{key}_benchmark" for key in directories]
+    create_tarballs(directories)
+
+    # mask directory cdl
+    mask_keep = ["tm_toa", "etm_sr", "oli_sr"]
+    mask_filenames = {
+        f"ssl4eo_l_{key.split('_')[0]}_cdl": val
+        for key, val in filenames.items()
+        if key in mask_keep
+    }
+    create_mask_directory(".", mask_filenames, "cdl")
+    directories = mask_filenames.keys()
+    create_tarballs(directories)
+
+    # mask directory nlcd
+    mask_filenames = {
+        f"ssl4eo_l_{key.split('_')[0]}_nlcd": val
+        for key, val in filenames.items()
+        if key in mask_keep
+    }
+    create_mask_directory(".", mask_filenames, "nlcd")
+    directories = mask_filenames.keys()
+    create_tarballs(directories)
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diff --git a/tests/datasets/test_ssl4eo_benchmark.py b/tests/datasets/test_ssl4eo_benchmark.py
new file mode 100644
index 00000000000..513687b95e4
--- /dev/null
+++ b/tests/datasets/test_ssl4eo_benchmark.py
@@ -0,0 +1,135 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import glob
+import os
+import shutil
+from itertools import product
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pytest
+import torch
+import torch.nn as nn
+from _pytest.fixtures import SubRequest
+from pytest import MonkeyPatch
+from torch.utils.data import ConcatDataset
+
+import torchgeo.datasets.utils
+from torchgeo.datasets import SSL4EOLBenchmark
+
+
+def download_url(url: str, root: str, *args: str, **kwargs: str) -> None:
+    shutil.copy(url, root)
+
+
+class TestSSL4EOLBenchmark:
+    @pytest.fixture(
+        params=product(
+            ["tm_toa", "etm_toa", "etm_sr", "oli_tirs_toa", "oli_sr"],
+            ["cdl", "nlcd"],
+            ["train", "val", "test"],
+        )
+    )
+    def dataset(
+        self, monkeypatch: MonkeyPatch, tmp_path: Path, request: SubRequest
+    ) -> SSL4EOLBenchmark:
+        monkeypatch.setattr(
+            torchgeo.datasets.ssl4eo_benchmark, "download_url", download_url
+        )
+        root = str(tmp_path)
+
+        url = os.path.join("tests", "data", "ssl4eo_benchmark_landsat", "{}.tar.gz")
+        monkeypatch.setattr(SSL4EOLBenchmark, "url", url)
+
+        input_sensor, mask_product, split = request.param
+        monkeypatch.setattr(
+            SSL4EOLBenchmark, "split_percentages", [1 / 3, 1 / 3, 1 / 3]
+        )
+
+        img_md5s = {
+            "tm_toa": "ecfdd3dcbc812c5e7cf272a5cddb33e9",
+            "etm_sr": "3e598245948eb7d072d5b83c95f22422",
+            "etm_toa": "e24ff11f6aedb3930380b53cb6f780b6",
+            "oli_tirs_toa": "490baa1eedd5032277e2a07f45dd8c2b",
+            "oli_sr": "884f6e28a23a1b7d464eff39abd7667d",
+        }
+        monkeypatch.setattr(SSL4EOLBenchmark, "img_md5s", img_md5s)
+
+        mask_md5s = {
+            "tm": {
+                "cdl": "43f30648e0f7c8dba78fa729b6db9ffe",
+                "nlcd": "4272958acb32cc3b83f593684bc3e63c",
+            },
+            "etm": {
+                "cdl": "b215b7e3b65b18a6d52ce9a35c90a16f",
+                "nlcd": "f823fc69965d7f6215f52bea2141df41",
+            },
+            "oli": {
+                "cdl": "aaa956d7aa985e8de2c565858c9ac4e8",
+                "nlcd": "cc49207df010a4f358fb16a46772e9ae",
+            },
+        }
+        monkeypatch.setattr(SSL4EOLBenchmark, "mask_md5s", mask_md5s)
+
+        transforms = nn.Identity()
+        return SSL4EOLBenchmark(
+            root=root,
+            input_sensor=input_sensor,
+            mask_product=mask_product,
+            split=split,
+            transforms=transforms,
+            download=True,
+            checksum=True,
+        )
+
+    def test_getitem(self, dataset: SSL4EOLBenchmark) -> None:
+        x = dataset[0]
+        assert isinstance(x, dict)
+        assert isinstance(x["image"], torch.Tensor)
+        assert isinstance(x["mask"], torch.Tensor)
+
+    def test_invalid_split(self) -> None:
+        with pytest.raises(AssertionError):
+            SSL4EOLBenchmark(split="foo")
+
+    def test_invalid_input_sensor(self) -> None:
+        with pytest.raises(AssertionError):
+            SSL4EOLBenchmark(input_sensor="foo")
+
+    def test_invalid_mask_product(self) -> None:
+        with pytest.raises(AssertionError):
+            SSL4EOLBenchmark(mask_product="foo")
+
+    def test_add(self, dataset: SSL4EOLBenchmark) -> None:
+        ds = dataset + dataset
+        assert isinstance(ds, ConcatDataset)
+
+    def test_already_extracted(self, dataset: SSL4EOLBenchmark) -> None:
+        SSL4EOLBenchmark(
+            root=dataset.root,
+            input_sensor=dataset.input_sensor,
+            mask_product=dataset.mask_product,
+            download=True,
+        )
+
+    def test_already_downloaded(self, tmp_path: Path) -> None:
+        pathname = os.path.join("tests", "data", "ssl4eo_benchmark_landsat", "*.tar.gz")
+        root = str(tmp_path)
+        for tarfile in glob.iglob(pathname):
+            shutil.copy(tarfile, root)
+        SSL4EOLBenchmark(root)
+
+    def test_not_downloaded(self, tmp_path: Path) -> None:
+        with pytest.raises(RuntimeError, match="Dataset not found"):
+            SSL4EOLBenchmark(str(tmp_path))
+
+    def test_plot(self, dataset: SSL4EOLBenchmark) -> None:
+        sample = dataset[0]
+        dataset.plot(sample, suptitle="Test")
+        plt.close()
+        dataset.plot(sample, show_titles=False)
+        plt.close()
+        sample["prediction"] = sample["mask"].clone()
+        dataset.plot(sample)
+        plt.close()
diff --git a/tests/trainers/test_segmentation.py b/tests/trainers/test_segmentation.py
index bc7f5e5b3ca..29ddf2dcadb 100644
--- a/tests/trainers/test_segmentation.py
+++ b/tests/trainers/test_segmentation.py
@@ -70,6 +70,8 @@ class TestSemanticSegmentationTask:
             "sen12ms_s2_all",
             "sen12ms_s2_reduced",
             "spacenet1",
+            "ssl4eo_l_benchmark_cdl",
+            "ssl4eo_l_benchmark_nlcd",
             "vaihingen2d",
         ],
     )
diff --git a/torchgeo/datamodules/__init__.py b/torchgeo/datamodules/__init__.py
index ba902b0cd61..8761e850e18 100644
--- a/torchgeo/datamodules/__init__.py
+++ b/torchgeo/datamodules/__init__.py
@@ -30,6 +30,7 @@
 from .so2sat import So2SatDataModule
 from .spacenet import SpaceNet1DataModule
 from .ssl4eo import SSL4EOLDataModule, SSL4EOS12DataModule
+from .ssl4eo_benchmark import SSL4EOLBenchmarkDataModule
 from .sustainbench_crop_yield import SustainBenchCropYieldDataModule
 from .ucmerced import UCMercedDataModule
 from .usavars import USAVarsDataModule
@@ -65,6 +66,7 @@
     "SKIPPDDataModule",
     "So2SatDataModule",
     "SpaceNet1DataModule",
+    "SSL4EOLBenchmarkDataModule",
     "SSL4EOLDataModule",
     "SSL4EOS12DataModule",
     "SustainBenchCropYieldDataModule",
diff --git a/torchgeo/datamodules/ssl4eo_benchmark.py b/torchgeo/datamodules/ssl4eo_benchmark.py
new file mode 100644
index 00000000000..40da15a4cde
--- /dev/null
+++ b/torchgeo/datamodules/ssl4eo_benchmark.py
@@ -0,0 +1,50 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""SSL4EO datamodule."""
+
+from typing import Any
+
+import kornia.augmentation as K
+
+from ..datasets import SSL4EOLBenchmark
+from ..transforms import AugmentationSequential
+from .geo import NonGeoDataModule
+
+
+class SSL4EOLBenchmarkDataModule(NonGeoDataModule):
+    """LightningDataModule implementation for the SSL4EO-L Benchmark dataset.
+
+    .. versionadded:: 0.5
+    """
+
+    crop_size = 224
+
+    def __init__(
+        self, batch_size: int = 64, num_workers: int = 0, **kwargs: Any
+    ) -> None:
+        """Initialize a new SSL4EOLBenchmarkDataModule instance.
+
+        Args:
+            batch_size: Size of each mini-batch.
+            num_workers: Number of workers for parallel data loading.
+            **kwargs: Additional keyword arguments passed to
+                :class:`~torchgeo.datasets.SSL4EOLBenchmark`.
+        """
+        super().__init__(SSL4EOLBenchmark, batch_size, num_workers, **kwargs)
+
+        self.train_aug = AugmentationSequential(
+            K.Normalize(mean=self.mean, std=self.std),
+            K.CenterCrop(self.crop_size),
+            data_keys=["image", "mask"],
+        )
+        self.val_aug = AugmentationSequential(
+            K.Normalize(mean=self.mean, std=self.std),
+            K.CenterCrop(self.crop_size),
+            data_keys=["image", "mask"],
+        )
+        self.test_aug = AugmentationSequential(
+            K.Normalize(mean=self.mean, std=self.std),
+            K.CenterCrop(self.crop_size),
+            data_keys=["image", "mask"],
+        )
diff --git a/torchgeo/datasets/__init__.py b/torchgeo/datasets/__init__.py
index 92734304a48..ae0efe90ea6 100644
--- a/torchgeo/datasets/__init__.py
+++ b/torchgeo/datasets/__init__.py
@@ -105,6 +105,7 @@
     time_series_split,
 )
 from .ssl4eo import SSL4EO, SSL4EOL, SSL4EOS12
+from .ssl4eo_benchmark import SSL4EOLBenchmark
 from .sustainbench_crop_yield import SustainBenchCropYield
 from .ucmerced import UCMerced
 from .usavars import USAVars
@@ -210,6 +211,7 @@
     "SpaceNet6",
     "SpaceNet7",
     "SSL4EO",
+    "SSL4EOLBenchmark",
     "SSL4EOL",
     "SSL4EOS12",
     "SustainBenchCropYield",
diff --git a/torchgeo/datasets/ssl4eo_benchmark.py b/torchgeo/datasets/ssl4eo_benchmark.py
new file mode 100644
index 00000000000..d80254d913e
--- /dev/null
+++ b/torchgeo/datasets/ssl4eo_benchmark.py
@@ -0,0 +1,366 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""Self-Supervised Learning for Earth Observation Benchmark Datasets."""
+
+import glob
+import os
+from typing import Callable, Optional
+
+import matplotlib.pyplot as plt
+import numpy as np
+import rasterio
+import torch
+from matplotlib.colors import ListedColormap
+from torch import Tensor
+
+from .cdl import CDL
+from .geo import NonGeoDataset
+from .nlcd import NLCD
+from .utils import download_url, extract_archive
+
+
+class SSL4EOLBenchmark(NonGeoDataset):
+    """SSL4EO Landsat Benchmark Evaluation Dataset.
+
+    Dataset is intended to be used for evaluation of SSL techniques. Each
+    benchmark dataset consists of 25,000 images with corresponding land
+    cover classification masks.
+
+    Dataset format:
+
+    * Input landsat image and single channel mask
+    * 25,000 total samples split into train, val, test (70%, 15%, 15%)
+    * NLCD dataset version has 17 classes
+    * CDL dataset version has 134 classes
+
+    Each patch has the following properties:
+
+    * 264 x 264 pixels
+    * Resampled to 30 m resolution (7920 x 7920 m)
+    * Single multispectral GeoTIFF file
+
+    .. versionadded:: 0.5
+    """
+
+    url = "https://huggingface.co/datasets/torchgeo/{}/resolve/main/{}.tar.gz"
+
+    valid_input_sensors = ["tm_toa", "etm_toa", "etm_sr", "oli_tirs_toa", "oli_sr"]
+    valid_mask_products = ["cdl", "nlcd"]
+    valid_splits = ["train", "val", "test"]
+
+    image_root = "ssl4eo_l_{}_benchmark"
+    img_md5s = {
+        "tm_toa": "8e3c5bcd56d3780a442f1332013b8d15",
+        "etm_toa": "1b051c7fe4d61c581b341370c9e76f1f",
+        "etm_sr": "a33d5e3b9c309980ef15d453235843ed",
+        "oli_tirs_toa": "6e9d7cf0392e1de2cbdb39962ba591aa",
+        "oli_sr": "0700cd15cc2366fe68c2f8c02fa09a15",
+    }
+
+    mask_dir_dict = {
+        "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 = {
+        "tm": {
+            "cdl": "3d676770ffb56c7e222a7192a652a846",
+            "nlcd": "261149d7614fcfdcb3be368eefa825c7",
+        },
+        "etm": {
+            "cdl": "dd2560b18b89dfe7f0e867fcf7217bd0",
+            "nlcd": "916f4a433df6c8abca15b45b60d005d3",
+        },
+        "oli": {
+            "cdl": "1cb057de6eafeca975deb35cb9fb036f",
+            "nlcd": "9de0d6d4d0b94313b80450f650813922",
+        },
+    }
+
+    year_dict = {
+        "tm_toa": 2011,
+        "etm_toa": 2019,
+        "etm_sr": 2019,
+        "oli_tirs_toa": 2019,
+        "oli_sr": 2019,
+    }
+
+    rgb_indices = {
+        "tm_toa": [2, 1, 0],
+        "etm_toa": [2, 1, 0],
+        "etm_sr": [2, 1, 0],
+        "oli_tirs_toa": [3, 2, 1],
+        "oli_sr": [3, 2, 1],
+    }
+
+    split_percentages = [0.7, 0.15, 0.15]
+
+    ordinal_label_map = {"nlcd": NLCD.ordinal_label_map, "cdl": CDL.ordinal_label_map}
+
+    cmaps = {"nlcd": NLCD.cmap, "cdl": CDL.cmap}
+
+    def __init__(
+        self,
+        root: str = "data",
+        input_sensor: str = "oli_sr",
+        mask_product: str = "cdl",
+        split: str = "train",
+        transforms: Optional[Callable[[dict[str, Tensor]], dict[str, Tensor]]] = None,
+        download: bool = False,
+        checksum: bool = False,
+    ) -> None:
+        """Initialize a new SSL4EO Landsat Benchmark instance.
+
+        Args:
+            root: root directory where dataset can be found
+            input_sensor: one of ['etm_toa', 'etm_sr', 'oli_tirs_toa, 'oli_sr']
+            mask_product: mask target one of ['cdl', 'nlcd']
+            split: dataset split, one of ['train', 'val', 'test']
+            transforms: a function/transform that takes input sample and its target as
+                entry and returns a transformed version
+            download: if True, download dataset and store it in the root directory
+            checksum: if True, check the MD5 after downloading files (may be slow)
+
+        Raises:
+            AssertionError: if any arguments are invalid
+        """
+        assert (
+            input_sensor in self.valid_input_sensors
+        ), f"Only supports one of {self.valid_input_sensors}, but found {input_sensor}."
+        self.input_sensor = input_sensor
+        assert (
+            mask_product in self.valid_mask_products
+        ), f"Only supports one of {self.valid_mask_products}, but found {mask_product}."
+        self.mask_product = mask_product
+        assert (
+            split in self.valid_splits
+        ), f"Only supports one of {self.valid_splits}, but found {split}."
+        self.split = split
+
+        self.root = root
+        self.transforms = transforms
+        self.download = download
+        self.checksum = checksum
+        self.img_dir_name = self.image_root.format(self.input_sensor)
+        self.mask_dir_name = self.mask_dir_dict[self.input_sensor].format(
+            self.mask_product
+        )
+
+        self._verify()
+
+        self.sample_collection = self.retrieve_sample_collection()
+
+        # train, val, test split
+        np.random.seed(0)
+        sizes = (np.array(self.split_percentages) * len(self.sample_collection)).astype(
+            int
+        )
+        cutoffs = np.cumsum(sizes)[:-1]
+        sample_indices = np.arange(len(self.sample_collection))
+        np.random.shuffle(sample_indices)
+        groups = np.split(sample_indices, cutoffs)
+        split_indices = {"train": groups[0], "val": groups[1], "test": groups[2]}[
+            self.split
+        ]
+
+        self.sample_collection = [self.sample_collection[idx] for idx in split_indices]
+
+    def _verify(self) -> None:
+        """Verify the integrity of the dataset.
+
+        Raises:
+            RuntimeError: if ``download=False`` but dataset is missing or checksum fails
+        """
+        # Check if the extracted files already exist
+        img_pathname = os.path.join(self.root, self.img_dir_name, "**", "all_bands.tif")
+        exists = []
+        exists.append(bool(glob.glob(img_pathname, recursive=True)))
+        mask_pathname = os.path.join(
+            self.root,
+            self.mask_dir_name,
+            "**",
+            f"{self.mask_product}_{self.year_dict[self.input_sensor]}.tif",
+        )
+        exists.append(bool(glob.glob(mask_pathname, recursive=True)))
+
+        if all(exists):
+            return
+        # Check if the tar.gz files have already been downloaded
+        exists = []
+        img_pathname = os.path.join(self.root, f"{self.img_dir_name}.tar.gz")
+        exists.append(os.path.exists(img_pathname))
+
+        mask_pathname = os.path.join(self.root, f"{self.mask_dir_name}.tar.gz")
+        exists.append(os.path.exists(mask_pathname))
+
+        if all(exists):
+            self._extract()
+            return
+
+        # Check if the user requested to download the dataset
+        if not self.download:
+            raise RuntimeError(
+                f"Dataset not found in `root={self.root}` and `download=False`, "
+                "either specify a different `root` directory or use `download=True` "
+                "to automatically download the dataset."
+            )
+
+        # Download the dataset
+        self._download()
+        self._extract()
+
+    def _download(self) -> None:
+        """Download the dataset."""
+        # download imagery
+        download_url(
+            self.url.format(self.img_dir_name, self.img_dir_name),
+            self.root,
+            md5=self.img_md5s[self.input_sensor] if self.checksum else None,
+        )
+        # download mask
+        download_url(
+            self.url.format(self.mask_dir_name, self.mask_dir_name),
+            self.root,
+            md5=self.mask_md5s[self.input_sensor.split("_")[0]][self.mask_product]
+            if self.checksum
+            else None,
+        )
+
+    def _extract(self) -> None:
+        """Extract the dataset."""
+        img_pathname = os.path.join(self.root, f"{self.img_dir_name}.tar.gz")
+        extract_archive(img_pathname)
+
+        mask_pathname = os.path.join(self.root, f"{self.mask_dir_name}.tar.gz")
+        extract_archive(mask_pathname)
+
+    def __getitem__(self, index: int) -> dict[str, Tensor]:
+        """Return an index within the dataset.
+
+        Args:
+            index: index to return
+
+        Returns:
+            image and sample
+        """
+        img_path, mask_path = self.sample_collection[index]
+
+        sample = {
+            "image": self._load_image(img_path),
+            "mask": self._load_mask(mask_path),
+        }
+
+        if self.transforms is not None:
+            sample = self.transforms(sample)
+
+        return sample
+
+    def __len__(self) -> int:
+        """Return the number of data points in the dataset.
+
+        Returns:
+            length of the dataset
+        """
+        return len(self.sample_collection)
+
+    def retrieve_sample_collection(self) -> list[tuple[str, str]]:
+        """Retrieve paths to samples in data directory."""
+        img_paths = glob.glob(
+            os.path.join(self.root, self.img_dir_name, "**", "all_bands.tif"),
+            recursive=True,
+        )
+        img_paths = sorted(img_paths)
+        sample_collection: list[tuple[str, str]] = []
+        for img_path in img_paths:
+            mask_path = img_path.replace(self.img_dir_name, self.mask_dir_name).replace(
+                "all_bands.tif",
+                f"{self.mask_product}_{self.year_dict[self.input_sensor]}.tif",
+            )
+            sample_collection.append((img_path, mask_path))
+        return sample_collection
+
+    def _load_image(self, path: str) -> Tensor:
+        """Load the input image.
+
+        Args:
+            path: path to input image
+
+        Returns:
+            image
+        """
+        with rasterio.open(path) as src:
+            image = src.read().astype(np.float32)
+        return torch.from_numpy(image)
+
+    def _load_mask(self, path: str) -> Tensor:
+        """Load the mask.
+
+        Args:
+            path: path to mask
+
+        Retuns:
+            mask
+        """
+        with rasterio.open(path) as src:
+            mask = src.read()
+
+        for k, v in self.ordinal_label_map[self.mask_product].items():
+            mask[mask == k] = v
+
+        return torch.from_numpy(mask).long()
+
+    def plot(
+        self,
+        sample: dict[str, Tensor],
+        show_titles: bool = True,
+        suptitle: Optional[str] = None,
+    ) -> plt.Figure:
+        """Plot a sample from the dataset.
+
+        Args:
+            sample: a sample returned by :meth:`__getitem__`
+            show_titles: flag indicating whether to show titles above each panel
+            suptitle: optional string to use as a suptitle
+
+        Returns:
+            a matplotlib Figure with the rendered sample
+        """
+        ncols = 2
+        image = sample["image"][self.rgb_indices[self.input_sensor]].permute(1, 2, 0)
+        image = image.numpy() / 255
+
+        plt_cmap = ListedColormap(
+            np.stack(
+                [np.array(val) / 255 for val in self.cmaps[self.mask_product].values()],
+                axis=0,
+            )
+        )
+
+        mask = sample["mask"].squeeze(0)
+
+        showing_predictions = "prediction" in sample
+        if showing_predictions:
+            prediction_mask = sample["prediction"].squeeze(0).numpy()
+            ncols = 3
+
+        fig, ax = plt.subplots(ncols=ncols, figsize=(4 * ncols, 4))
+        ax[0].imshow(image)
+        ax[0].axis("off")
+        ax[1].imshow(mask, cmap=plt_cmap)
+        ax[1].axis("off")
+        if show_titles:
+            ax[0].set_title("Image")
+            ax[1].set_title("Mask")
+
+        if showing_predictions:
+            ax[2].imshow(prediction_mask, cmap=plt_cmap)
+            if show_titles:
+                ax[2].set_title("Prediction")
+
+        if suptitle is not None:
+            plt.suptitle(suptitle)
+
+        return fig