diff --git a/torchgeo/datamodules/oscd.py b/torchgeo/datamodules/oscd.py
index 87ae20cdf40..fea9602adeb 100644
--- a/torchgeo/datamodules/oscd.py
+++ b/torchgeo/datamodules/oscd.py
@@ -86,9 +86,11 @@ def __init__(
         self.std = torch.tensor([STD[b] for b in self.bands])
 
         self.aug = AugmentationSequential(
-            K.Normalize(mean=self.mean, std=self.std),
-            _RandomNCrop(self.patch_size, batch_size),
-            data_keys=['image1', 'image2', 'mask'],
+            K.VideoSequential(
+                K.Normalize(mean=self.mean, std=self.std),
+                _RandomNCrop(self.patch_size, batch_size),
+            ),
+            data_keys=['image', 'mask'],
         )
 
     def setup(self, stage: str) -> None:
diff --git a/torchgeo/datasets/oscd.py b/torchgeo/datasets/oscd.py
index 28f7714a7c6..8a6616534eb 100644
--- a/torchgeo/datasets/oscd.py
+++ b/torchgeo/datasets/oscd.py
@@ -150,7 +150,8 @@ def __getitem__(self, index: int) -> dict[str, Tensor]:
         image1 = self._load_image(files['images1'])
         image2 = self._load_image(files['images2'])
         mask = self._load_target(str(files['mask']))
-        sample = {'image1': image1, 'image2': image2, 'mask': mask}
+        image = torch.stack(tensors=[image1, image2], dim=0)
+        sample = {'image': image, 'mask': mask}
 
         if self.transforms is not None:
             sample = self.transforms(sample)
@@ -169,7 +170,8 @@ def _load_files(self) -> list[dict[str, str | Sequence[str]]]:
         regions = []
         labels_root = os.path.join(
             self.root,
-            f'Onera Satellite Change Detection dataset - {self.split.capitalize()} '
+            f'Onera Satellite Change Detection dataset - {
+                self.split.capitalize()} '
             + 'Labels',
         )
         images_root = os.path.join(
diff --git a/torchgeo/transforms/transforms.py b/torchgeo/transforms/transforms.py
index d8f80bdcaac..e04389d8b18 100644
--- a/torchgeo/transforms/transforms.py
+++ b/torchgeo/transforms/transforms.py
@@ -102,7 +102,13 @@ def forward(self, batch: dict[str, Any]) -> dict[str, Any]:
             batch['boxes'] = Boxes(batch['boxes']).to_tensor(mode='xyxy')
 
         # Torchmetrics does not support masks with a channel dimension
-        if 'mask' in batch and batch['mask'].shape[1] == 1:
+        # Kornia adds a temporal dimension to mask when passed through VideoSequential.
+        if 'mask' in batch and batch['mask'].ndim == 5:
+            if batch['mask'].shape[1] == 1:
+                batch['mask'] = rearrange(batch['mask'], 'b () c h w -> b c h w')
+            if batch['mask'].shape[1] == 1:
+                batch['mask'] = rearrange(batch['mask'], 'b () h w -> b h w')
+        elif 'mask' in batch and batch['mask'].shape[1] == 1:
             batch['mask'] = rearrange(batch['mask'], 'b () h w -> b h w')
         if 'masks' in batch and batch['masks'].ndim == 4:
             batch['masks'] = rearrange(batch['masks'], '() c h w -> c h w')