diff --git a/torchgeo/datamodules/cyclone.py b/torchgeo/datamodules/cyclone.py
index b4fe1993310..5ded6c05265 100644
--- a/torchgeo/datamodules/cyclone.py
+++ b/torchgeo/datamodules/cyclone.py
@@ -5,7 +5,6 @@
 
 from typing import Any, Dict, Optional
 
-import matplotlib.pyplot as plt
 import pytorch_lightning as pl
 import torch
 from sklearn.model_selection import GroupShuffleSplit
@@ -149,10 +148,3 @@ def test_dataloader(self) -> DataLoader[Any]:
             num_workers=self.num_workers,
             shuffle=False,
         )
-
-    def plot(self, *args: Any, **kwargs: Any) -> plt.Figure:
-        """Run :meth:`torchgeo.datasets.TropicalCycloneWindEstimation.plot`.
-
-        .. versionadded:: 0.4
-        """
-        return self.all_train_dataset.plot(*args, **kwargs)
diff --git a/torchgeo/datamodules/deepglobelandcover.py b/torchgeo/datamodules/deepglobelandcover.py
index 955467048e3..1a5de89aee4 100644
--- a/torchgeo/datamodules/deepglobelandcover.py
+++ b/torchgeo/datamodules/deepglobelandcover.py
@@ -5,7 +5,6 @@
 
 from typing import Any, Dict, Optional
 
-import matplotlib.pyplot as plt
 import pytorch_lightning as pl
 from torch.utils.data import DataLoader, Dataset
 from torchvision.transforms import Compose
@@ -123,10 +122,3 @@ def test_dataloader(self) -> DataLoader[Dict[str, Any]]:
             num_workers=self.num_workers,
             shuffle=False,
         )
-
-    def plot(self, *args: Any, **kwargs: Any) -> plt.Figure:
-        """Run :meth:`torchgeo.datasets.DeepGlobeLandCover.plot`.
-
-        .. versionadded:: 0.4
-        """
-        return self.test_dataset.plot(*args, **kwargs)
diff --git a/torchgeo/datamodules/oscd.py b/torchgeo/datamodules/oscd.py
index fbe4ff09d23..e604eed767c 100644
--- a/torchgeo/datamodules/oscd.py
+++ b/torchgeo/datamodules/oscd.py
@@ -6,7 +6,6 @@
 from typing import Any, Dict, List, Optional, Tuple
 
 import kornia.augmentation as K
-import matplotlib.pyplot as plt
 import pytorch_lightning as pl
 import torch
 from einops import repeat
@@ -197,10 +196,3 @@ def test_dataloader(self) -> DataLoader[Any]:
         return DataLoader(
             self.test_dataset, batch_size=1, num_workers=self.num_workers, shuffle=False
         )
-
-    def plot(self, *args: Any, **kwargs: Any) -> plt.Figure:
-        """Run :meth:`torchgeo.datasets.OSCD.plot`.
-
-        .. versionadded:: 0.4
-        """
-        return self.test_dataset.plot(*args, **kwargs)
diff --git a/torchgeo/datamodules/sen12ms.py b/torchgeo/datamodules/sen12ms.py
index 3d3360f0008..e72d295acec 100644
--- a/torchgeo/datamodules/sen12ms.py
+++ b/torchgeo/datamodules/sen12ms.py
@@ -5,7 +5,6 @@
 
 from typing import Any, Dict, Optional
 
-import matplotlib.pyplot as plt
 import pytorch_lightning as pl
 import torch
 from sklearn.model_selection import GroupShuffleSplit
@@ -192,10 +191,3 @@ def test_dataloader(self) -> DataLoader[Any]:
             num_workers=self.num_workers,
             shuffle=False,
         )
-
-    def plot(self, *args: Any, **kwargs: Any) -> plt.Figure:
-        """Run :meth:`torchgeo.datasets.SEN12MS.plot`.
-
-        .. versionadded:: 0.4
-        """
-        return self.all_train_dataset.plot(*args, **kwargs)
diff --git a/torchgeo/datasets/cyclone.py b/torchgeo/datasets/cyclone.py
index c27c383284f..e12a3359d6d 100644
--- a/torchgeo/datasets/cyclone.py
+++ b/torchgeo/datasets/cyclone.py
@@ -232,7 +232,6 @@ def plot(
         .. versionadded:: 0.2
         """
         image, label = sample["image"], sample["label"]
-        image = image.permute((1, 2, 0)).numpy()
 
         showing_predictions = "prediction" in sample
         if showing_predictions:
diff --git a/torchgeo/datasets/utils.py b/torchgeo/datasets/utils.py
index 2335dd16060..1e7f43bbf87 100644
--- a/torchgeo/datasets/utils.py
+++ b/torchgeo/datasets/utils.py
@@ -645,7 +645,7 @@ def draw_semantic_segmentation_masks(
     classes = torch.from_numpy(np.arange(len(colors) if colors else 0, dtype=np.uint8))
     class_masks = mask == classes[:, None, None]
     img = draw_segmentation_masks(
-        image=image.uint8(), masks=class_masks, alpha=alpha, colors=colors
+        image=image.byte(), masks=class_masks, alpha=alpha, colors=colors
     )
     img = img.permute((1, 2, 0)).numpy().astype(np.uint8)
     return cast("np.typing.NDArray[np.uint8]", img)