diff --git a/torchgeo/datamodules/substation.py b/torchgeo/datamodules/substation.py
index 37c72d6e952..2ff369acd44 100644
--- a/torchgeo/datamodules/substation.py
+++ b/torchgeo/datamodules/substation.py
@@ -75,7 +75,15 @@ def __init__(
         self.image_resize = image_resize
         self.mask_resize = mask_resize
         self.num_of_timepoints = num_of_timepoints
-
+        self.geo_transforms = geo_transforms if geo_transforms is not None else self._identity
+        self.color_transforms = color_transforms if color_transforms is not None else self._identity
+        self.image_resize = image_resize if image_resize is not None else self._identity
+        self.mask_resize = mask_resize if mask_resize is not None else self._identity
+
+    def _identity(self, x: torch.Tensor) -> torch.Tensor:
+        """Identity function for default transformations."""
+        return x
+    
     def setup(self, stage: str) -> None:
         """Set up datasets.
 
@@ -98,7 +106,6 @@ def setup(self, stage: str) -> None:
         val_len = int(total_len * self.val_split_pct)
         test_len = int(total_len * self.test_split_pct)
         train_len = total_len - val_len - test_len
-        print(val_len, test_len, train_len)
 
         self.train_dataset, self.val_dataset, self.test_dataset = random_split(
             dataset, [train_len, val_len, test_len], generator
@@ -123,12 +130,17 @@ def _apply_transforms(self, dataset: Subset[Any]) -> Subset[Any]:
             image, mask = sample['image'], sample['mask']
 
             if self.geo_transforms:
+                print(self.geo_transforms)
+                if mask.shape.__len__() == 2:
+                    mask = mask.unsqueeze(0)
                 combined = torch.cat((image, mask), 0)
                 combined = self.geo_transforms(combined)
                 image, mask = torch.split(combined, [image.shape[0], mask.shape[0]], 0)
+                if mask.shape[0] == 1:
+                    mask = mask.squeeze(0)
 
             if self.color_transforms:
-                num_timepoints = image.shape[0] // self.bands
+                num_timepoints = image.shape[0] // self.bands.__len__()
                 for i in range(num_timepoints):
                     start = i * len(self.bands)
                     end = start + 3