diff --git a/pyproject.toml b/pyproject.toml
index ad096b8d987..0762fd6dfaf 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -102,6 +102,10 @@ filterwarnings = [
     "ignore:The dataloader, .*, does not have many workers which may be a bottleneck:UserWarning",
     # pytorch-lightning warns us about using the CPU when a GPU is available
     "ignore:GPU available but not used.:UserWarning",
+    # https://github.com/kornia/kornia/pull/1611
+    "ignore:`ColorJitter` is now following Torchvision implementation.:DeprecationWarning:kornia.augmentation._2d.intensity.color_jitter",
+    # https://github.com/kornia/kornia/pull/1663
+    "ignore:`RandomGaussianBlur` has changed its behavior and now randomly sample sigma for both axes.:DeprecationWarning:kornia.augmentation._2d.intensity.gaussian_blur",
 
     # Unexpected warnings, worth investigating
     # pytorch-lightning is having trouble inferring the batch size for ChesapeakeCVPRDataModule and CycloneDataModule for some reason
diff --git a/requirements/required.txt b/requirements/required.txt
index 660adf05260..d5e0b24e0fd 100644
--- a/requirements/required.txt
+++ b/requirements/required.txt
@@ -4,7 +4,7 @@ setuptools==67.3.1
 # install
 einops==0.6.0
 fiona==1.9.1
-kornia==0.6.9
+kornia==0.6.10
 matplotlib==3.7.0
 numpy==1.24.2
 omegaconf==2.3.0
diff --git a/tests/transforms/test_indices.py b/tests/transforms/test_indices.py
index b3c0f8eb7e1..c58cf0887d5 100644
--- a/tests/transforms/test_indices.py
+++ b/tests/transforms/test_indices.py
@@ -30,7 +30,7 @@
 def sample() -> Dict[str, Tensor]:
     return {
         "image": torch.arange(3 * 4 * 4, dtype=torch.float).view(3, 4, 4),
-        "mask": torch.arange(4 * 4, dtype=torch.long).view(4, 4),
+        "mask": torch.arange(4 * 4, dtype=torch.long).view(1, 4, 4),
     }
 
 
@@ -38,7 +38,7 @@ def sample() -> Dict[str, Tensor]:
 def batch() -> Dict[str, Tensor]:
     return {
         "image": torch.arange(2 * 3 * 4 * 4, dtype=torch.float).view(2, 3, 4, 4),
-        "mask": torch.arange(2 * 4 * 4, dtype=torch.long).view(2, 4, 4),
+        "mask": torch.arange(2 * 4 * 4, dtype=torch.long).view(2, 1, 4, 4),
     }
 
 
diff --git a/torchgeo/datamodules/landcoverai.py b/torchgeo/datamodules/landcoverai.py
index 0ac1ec385ac..c6994f99d54 100644
--- a/torchgeo/datamodules/landcoverai.py
+++ b/torchgeo/datamodules/landcoverai.py
@@ -37,14 +37,7 @@ def __init__(
             K.RandomHorizontalFlip(p=0.5),
             K.RandomVerticalFlip(p=0.5),
             K.RandomSharpness(p=0.5),
-            K.ColorJitter(
-                p=0.5,
-                brightness=0.1,
-                contrast=0.1,
-                saturation=0.1,
-                hue=0.1,
-                silence_instantiation_warning=True,
-            ),
+            K.ColorJitter(p=0.5, brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
             data_keys=["image", "mask"],
         )
         self.aug = AugmentationSequential(
diff --git a/torchgeo/datamodules/resisc45.py b/torchgeo/datamodules/resisc45.py
index cb1e9553553..564ca07b739 100644
--- a/torchgeo/datamodules/resisc45.py
+++ b/torchgeo/datamodules/resisc45.py
@@ -42,13 +42,6 @@ def __init__(
             K.RandomVerticalFlip(p=0.5),
             K.RandomSharpness(p=0.5),
             K.RandomErasing(p=0.1),
-            K.ColorJitter(
-                p=0.5,
-                brightness=0.1,
-                contrast=0.1,
-                saturation=0.1,
-                hue=0.1,
-                silence_instantiation_warning=True,
-            ),
+            K.ColorJitter(p=0.5, brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
             data_keys=["image"],
         )
diff --git a/torchgeo/datamodules/spacenet.py b/torchgeo/datamodules/spacenet.py
index 802cc7a26c6..90a6f6f5ac6 100644
--- a/torchgeo/datamodules/spacenet.py
+++ b/torchgeo/datamodules/spacenet.py
@@ -52,14 +52,7 @@ def __init__(
             K.RandomHorizontalFlip(p=0.5),
             K.RandomVerticalFlip(p=0.5),
             K.RandomSharpness(p=0.5),
-            K.ColorJitter(
-                p=0.5,
-                brightness=0.1,
-                contrast=0.1,
-                saturation=0.1,
-                hue=0.1,
-                silence_instantiation_warning=True,
-            ),
+            K.ColorJitter(p=0.5, brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
             data_keys=["image", "mask"],
         )
         self.aug = AugmentationSequential(