diff --git a/tests/datasets/test_advance.py b/tests/datasets/test_advance.py
index 30387142b53..ff2283c83d3 100644
--- a/tests/datasets/test_advance.py
+++ b/tests/datasets/test_advance.py
@@ -7,6 +7,7 @@
 from pathlib import Path
 from typing import Any, Generator
 
+import matplotlib.pyplot as plt
 import pytest
 import torch
 import torch.nn as nn
@@ -86,3 +87,13 @@ def test_mock_missing_module(
             match="scipy is not installed and is required to use this dataset",
         ):
             dataset[0]
+
+    def test_plot(self, dataset: ADVANCE) -> None:
+        x = dataset[0].copy()
+        dataset.plot(x, suptitle="Test")
+        plt.close()
+        dataset.plot(x, show_titles=False)
+        plt.close()
+        x["prediction"] = x["label"].clone()
+        dataset.plot(x)
+        plt.close()
diff --git a/tests/datasets/test_benin_cashews.py b/tests/datasets/test_benin_cashews.py
index f4f6d285f2b..ea19ad3a378 100644
--- a/tests/datasets/test_benin_cashews.py
+++ b/tests/datasets/test_benin_cashews.py
@@ -7,6 +7,7 @@
 from pathlib import Path
 from typing import Generator
 
+import matplotlib.pyplot as plt
 import pytest
 import torch
 import torch.nn as nn
@@ -49,9 +50,12 @@ def dataset(
         )
         root = str(tmp_path)
         transforms = nn.Identity()  # type: ignore[attr-defined]
+        bands = BeninSmallHolderCashews.ALL_BANDS
+
         return BeninSmallHolderCashews(
             root,
             transforms=transforms,
+            bands=bands,
             download=True,
             api_key="",
             checksum=True,
@@ -87,3 +91,19 @@ def test_invalid_bands(self) -> None:
 
         with pytest.raises(ValueError, match="is an invalid band name."):
             BeninSmallHolderCashews(bands=("foo", "bar"))
+
+    def test_plot(self, dataset: BeninSmallHolderCashews) -> None:
+        x = dataset[0].copy()
+        dataset.plot(x, suptitle="Test")
+        plt.close()
+        dataset.plot(x, show_titles=False)
+        plt.close()
+        x["prediction"] = x["mask"].clone()
+        dataset.plot(x)
+        plt.close()
+
+    def test_failed_plot(self, dataset: BeninSmallHolderCashews) -> None:
+        single_band_dataset = BeninSmallHolderCashews(root=dataset.root, bands=("B01",))
+        with pytest.raises(ValueError, match="Dataset doesn't contain"):
+            x = single_band_dataset[0].copy()
+            single_band_dataset.plot(x, suptitle="Test")
diff --git a/tests/datasets/test_bigearthnet.py b/tests/datasets/test_bigearthnet.py
index d1f9ca80f68..84307a484a6 100644
--- a/tests/datasets/test_bigearthnet.py
+++ b/tests/datasets/test_bigearthnet.py
@@ -6,6 +6,7 @@
 from pathlib import Path
 from typing import Generator
 
+import matplotlib.pyplot as plt
 import pytest
 import torch
 import torch.nn as nn
@@ -148,3 +149,13 @@ def test_not_downloaded(self, tmp_path: Path) -> None:
         "to automaticaly download the dataset."
         with pytest.raises(RuntimeError, match=err):
             BigEarthNet(str(tmp_path))
+
+    def test_plot(self, dataset: BigEarthNet) -> None:
+        x = dataset[0].copy()
+        dataset.plot(x, suptitle="Test")
+        plt.close()
+        dataset.plot(x, show_titles=False)
+        plt.close()
+        x["prediction"] = x["label"].clone()
+        dataset.plot(x)
+        plt.close()
diff --git a/torchgeo/datasets/advance.py b/torchgeo/datasets/advance.py
index e5eee11b97b..c22819b0b7d 100644
--- a/torchgeo/datasets/advance.py
+++ b/torchgeo/datasets/advance.py
@@ -5,8 +5,9 @@
 
 import glob
 import os
-from typing import Callable, Dict, List, Optional
+from typing import Callable, Dict, List, Optional, cast
 
+import matplotlib.pyplot as plt
 import numpy as np
 import torch
 from PIL import Image
@@ -229,3 +230,43 @@ def _download(self) -> None:
             download_and_extract_archive(
                 url, self.root, filename=filename, md5=md5 if self.checksum else None
             )
+
+    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
+
+        .. versionadded:: 0.2
+        """
+        image = np.rollaxis(sample["image"].numpy(), 0, 3)
+        label = cast(int, sample["label"].item())
+        label_class = self.classes[label]
+
+        showing_predictions = "prediction" in sample
+        if showing_predictions:
+            prediction = cast(int, sample["prediction"].item())
+            prediction_class = self.classes[prediction]
+
+        fig, ax = plt.subplots(figsize=(4, 4))
+        ax.imshow(image)
+        ax.axis("off")
+        if show_titles:
+            title = f"Label: {label_class}"
+            if showing_predictions:
+                title += f"\nPrediction: {prediction_class}"
+            ax.set_title(title)
+
+        if suptitle is not None:
+            plt.suptitle(suptitle)
+        return fig
diff --git a/torchgeo/datasets/benin_cashews.py b/torchgeo/datasets/benin_cashews.py
index fffcc7f67c2..3d968de12db 100644
--- a/torchgeo/datasets/benin_cashews.py
+++ b/torchgeo/datasets/benin_cashews.py
@@ -8,6 +8,7 @@
 from functools import lru_cache
 from typing import Callable, Dict, Optional, Tuple
 
+import matplotlib.pyplot as plt
 import numpy as np
 import rasterio
 import rasterio.features
@@ -135,7 +136,7 @@ class BeninSmallHolderCashews(VisionDataset):
         "2020_10_30",
     )
 
-    band_names = (
+    ALL_BANDS = (
         "B01",
         "B02",
         "B03",
@@ -150,16 +151,17 @@ class BeninSmallHolderCashews(VisionDataset):
         "B12",
         "CLD",
     )
-
-    class_names = {
-        0: "No data",
-        1: "Well-managed planatation",
-        2: "Poorly-managed planatation",
-        3: "Non-planatation",
-        4: "Residential",
-        5: "Background",
-        6: "Uncertain",
-    }
+    RGB_BANDS = ("B04", "B03", "B02")
+
+    classes = [
+        "No data",
+        "Well-managed planatation",
+        "Poorly-managed planatation",
+        "Non-planatation",
+        "Residential",
+        "Background",
+        "Uncertain",
+    ]
 
     # Same for all tiles
     tile_height = 1186
@@ -170,7 +172,7 @@ def __init__(
         root: str = "data",
         chip_size: int = 256,
         stride: int = 128,
-        bands: Tuple[str, ...] = band_names,
+        bands: Tuple[str, ...] = ALL_BANDS,
         transforms: Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]] = None,
         download: bool = False,
         api_key: Optional[str] = None,
@@ -273,11 +275,11 @@ def _validate_bands(self, bands: Tuple[str, ...]) -> None:
         """
         assert isinstance(bands, tuple), "The list of bands must be a tuple"
         for band in bands:
-            if band not in self.band_names:
+            if band not in self.ALL_BANDS:
                 raise ValueError(f"'{band}' is an invalid band name.")
 
     @lru_cache(maxsize=128)
-    def _load_all_imagery(self, bands: Tuple[str, ...] = band_names) -> Tensor:
+    def _load_all_imagery(self, bands: Tuple[str, ...] = ALL_BANDS) -> Tensor:
         """Load all the imagery (across time) for the dataset.
 
         Optionally allows for subsetting of the bands that are loaded.
@@ -410,3 +412,68 @@ def _download(self, api_key: Optional[str] = None) -> None:
         target_archive_path = os.path.join(self.root, self.target_meta["filename"])
         for fn in [image_archive_path, target_archive_path]:
             extract_archive(fn, self.root)
+
+    def plot(
+        self,
+        sample: Dict[str, Tensor],
+        show_titles: bool = True,
+        time_step: int = 0,
+        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
+            time_step: time step at which to access image, beginning with 0
+            suptitle: optional string to use as a suptitle
+
+        Returns:
+            a matplotlib Figure with the rendered sample
+
+        Raises:
+            ValueError: if the RGB bands are not included in ``self.bands``
+
+        .. versionadded:: 0.2
+        """
+        rgb_indices = []
+        for band in self.RGB_BANDS:
+            if band in self.bands:
+                rgb_indices.append(self.bands.index(band))
+            else:
+                raise ValueError("Dataset doesn't contain some of the RGB bands")
+
+        num_time_points = sample["image"].shape[0]
+        assert time_step < num_time_points
+
+        image = np.rollaxis(sample["image"][time_step, rgb_indices].numpy(), 0, 3)
+        image = np.clip(image / 3000, 0, 1)
+        mask = sample["mask"].numpy()
+
+        num_panels = 2
+        showing_predictions = "prediction" in sample
+        if showing_predictions:
+            predictions = sample["prediction"].numpy()
+            num_panels += 1
+
+        fig, axs = plt.subplots(ncols=num_panels, figsize=(4 * num_panels, 4))
+
+        axs[0].imshow(image)
+        axs[0].axis("off")
+        if show_titles:
+            axs[0].set_title(f"t={time_step}")
+
+        axs[1].imshow(mask, vmin=0, vmax=6, interpolation="none")
+        axs[1].axis("off")
+        if show_titles:
+            axs[1].set_title("Mask")
+
+        if showing_predictions:
+            axs[2].imshow(predictions, vmin=0, vmax=6, interpolation="none")
+            axs[2].axis("off")
+            if show_titles:
+                axs[2].set_title("Predictions")
+
+        if suptitle is not None:
+            plt.suptitle(suptitle)
+        return fig
diff --git a/torchgeo/datasets/bigearthnet.py b/torchgeo/datasets/bigearthnet.py
index 48d24766553..2ef2f8285ad 100644
--- a/torchgeo/datasets/bigearthnet.py
+++ b/torchgeo/datasets/bigearthnet.py
@@ -8,6 +8,7 @@
 import os
 from typing import Callable, Dict, List, Optional
 
+import matplotlib.pyplot as plt
 import numpy as np
 import rasterio
 import torch
@@ -118,74 +119,77 @@ class BigEarthNet(VisionDataset):
 
     """
 
-    classes_43 = [
-        "Agro-forestry areas",
-        "Airports",
-        "Annual crops associated with permanent crops",
-        "Bare rock",
-        "Beaches, dunes, sands",
-        "Broad-leaved forest",
-        "Burnt areas",
-        "Coastal lagoons",
-        "Complex cultivation patterns",
-        "Coniferous forest",
-        "Construction sites",
-        "Continuous urban fabric",
-        "Discontinuous urban fabric",
-        "Dump sites",
-        "Estuaries",
-        "Fruit trees and berry plantations",
-        "Green urban areas",
-        "Industrial or commercial units",
-        "Inland marshes",
-        "Intertidal flats",
-        "Land principally occupied by agriculture, with significant areas of "
-        "natural vegetation",
-        "Mineral extraction sites",
-        "Mixed forest",
-        "Moors and heathland",
-        "Natural grassland",
-        "Non-irrigated arable land",
-        "Olive groves",
-        "Pastures",
-        "Peatbogs",
-        "Permanently irrigated land",
-        "Port areas",
-        "Rice fields",
-        "Road and rail networks and associated land",
-        "Salines",
-        "Salt marshes",
-        "Sclerophyllous vegetation",
-        "Sea and ocean",
-        "Sparsely vegetated areas",
-        "Sport and leisure facilities",
-        "Transitional woodland/shrub",
-        "Vineyards",
-        "Water bodies",
-        "Water courses",
-    ]
-    classes_19 = [
-        "Urban fabric",
-        "Industrial or commercial units",
-        "Arable land",
-        "Permanent crops",
-        "Pastures",
-        "Complex cultivation patterns",
-        "Land principally occupied by agriculture, with significant areas of natural "
-        "vegetation",
-        "Agro-forestry areas",
-        "Broad-leaved forest",
-        "Coniferous forest",
-        "Mixed forest",
-        "Natural grassland and sparsely vegetated areas",
-        "Moors, heathland and sclerophyllous vegetation",
-        "Transitional woodland, shrub",
-        "Beaches, dunes, sands",
-        "Inland wetlands",
-        "Coastal wetlands",
-        "Inland waters",
-        "Marine waters",
-    ]
+    class_sets = {
+        19: [
+            "Urban fabric",
+            "Industrial or commercial units",
+            "Arable land",
+            "Permanent crops",
+            "Pastures",
+            "Complex cultivation patterns",
+            "Land principally occupied by agriculture, with significant areas of"
+            " natural vegetation",
+            "Agro-forestry areas",
+            "Broad-leaved forest",
+            "Coniferous forest",
+            "Mixed forest",
+            "Natural grassland and sparsely vegetated areas",
+            "Moors, heathland and sclerophyllous vegetation",
+            "Transitional woodland, shrub",
+            "Beaches, dunes, sands",
+            "Inland wetlands",
+            "Coastal wetlands",
+            "Inland waters",
+            "Marine waters",
+        ],
+        43: [
+            "Agro-forestry areas",
+            "Airports",
+            "Annual crops associated with permanent crops",
+            "Bare rock",
+            "Beaches, dunes, sands",
+            "Broad-leaved forest",
+            "Burnt areas",
+            "Coastal lagoons",
+            "Complex cultivation patterns",
+            "Coniferous forest",
+            "Construction sites",
+            "Continuous urban fabric",
+            "Discontinuous urban fabric",
+            "Dump sites",
+            "Estuaries",
+            "Fruit trees and berry plantations",
+            "Green urban areas",
+            "Industrial or commercial units",
+            "Inland marshes",
+            "Intertidal flats",
+            "Land principally occupied by agriculture, with significant areas of"
+            " natural vegetation",
+            "Mineral extraction sites",
+            "Mixed forest",
+            "Moors and heathland",
+            "Natural grassland",
+            "Non-irrigated arable land",
+            "Olive groves",
+            "Pastures",
+            "Peatbogs",
+            "Permanently irrigated land",
+            "Port areas",
+            "Rice fields",
+            "Road and rail networks and associated land",
+            "Salines",
+            "Salt marshes",
+            "Sclerophyllous vegetation",
+            "Sea and ocean",
+            "Sparsely vegetated areas",
+            "Sport and leisure facilities",
+            "Transitional woodland/shrub",
+            "Vineyards",
+            "Water bodies",
+            "Water courses",
+        ],
+    }
+
     label_converter = {
         0: 0,
         1: 0,
@@ -220,6 +224,7 @@ class BigEarthNet(VisionDataset):
         41: 18,
         42: 18,
     }
+
     splits_metadata = {
         "train": {
             "url": "https://git.tu-berlin.de/rsim/BigEarthNet-MM_19-classes_models/-/raw/master/splits/train.csv?inline=false",  # noqa: E501
@@ -285,7 +290,7 @@ def __init__(
         self.transforms = transforms
         self.download = download
         self.checksum = checksum
-        self.class2idx = {c: i for i, c in enumerate(self.classes_43)}
+        self.class2idx = {c: i for i, c in enumerate(self.class_sets[43])}
         self._verify()
         self.folders = self._load_folders()
 
@@ -504,3 +509,71 @@ def _extract(self, filepath: str) -> None:
         """
         if not filepath.endswith(".csv"):
             extract_archive(filepath)
+
+    def _onehot_labels_to_names(
+        self, label_mask: "np.typing.NDArray[np.bool_]"
+    ) -> List[str]:
+        """Gets a list of class names given a label mask.
+
+        Args:
+            label_mask: a boolean mask corresponding to a set of labels or predictions
+
+        Returns
+            a list of class names corresponding to the input mask
+        """
+        labels = []
+        for i, mask in enumerate(label_mask):
+            if mask:
+                labels.append(self.class_sets[self.num_classes][i])
+        return labels
+
+    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
+
+        Raises:
+            ValueError: if ``self.bands`` is "s1"
+
+        .. versionadded:: 0.2
+        """
+        if self.bands == "s2":
+            image = np.rollaxis(sample["image"][[3, 2, 1]].numpy(), 0, 3)
+            image = np.clip(image / 2000, 0, 1)
+        elif self.bands == "all":
+            image = np.rollaxis(sample["image"][[5, 4, 3]].numpy(), 0, 3)
+            image = np.clip(image / 2000, 0, 1)
+        elif self.bands == "s1":
+            image = sample["image"][0].numpy()
+
+        label_mask = sample["label"].numpy().astype(np.bool_)
+        labels = self._onehot_labels_to_names(label_mask)
+
+        showing_predictions = "prediction" in sample
+        if showing_predictions:
+            prediction_mask = sample["prediction"].numpy().astype(np.bool_)
+            predictions = self._onehot_labels_to_names(prediction_mask)
+
+        fig, ax = plt.subplots(figsize=(4, 4))
+        ax.imshow(image)
+        ax.axis("off")
+        if show_titles:
+            title = f"Labels: {', '.join(labels)}"
+            if showing_predictions:
+                title += f"\nPredictions: {', '.join(predictions)}"
+            ax.set_title(title)
+
+        if suptitle is not None:
+            plt.suptitle(suptitle)
+        return fig