diff --git a/tests/datasets/test_utils.py b/tests/datasets/test_utils.py
index 631897bb7f2..cd6d2b36a24 100644
--- a/tests/datasets/test_utils.py
+++ b/tests/datasets/test_utils.py
@@ -31,6 +31,7 @@
     merge_samples,
     percentile_normalization,
     stack_samples,
+    unbind_samples,
     working_dir,
 )
 
@@ -457,7 +458,7 @@ def samples(self) -> List[Dict[str, Any]]:
             },
         ]
 
-    def test_stack_samples(self, samples: List[Dict[str, Any]]) -> None:
+    def test_stack_unbind_samples(self, samples: List[Dict[str, Any]]) -> None:
         sample = stack_samples(samples)
         assert sample["image"].size() == torch.Size(  # type: ignore[attr-defined]
             [2, 3]
@@ -468,6 +469,13 @@ def test_stack_samples(self, samples: List[Dict[str, Any]]) -> None:
         )
         assert sample["crs"] == [CRS.from_epsg(2000), CRS.from_epsg(2001)]
 
+        new_samples = unbind_samples(sample)
+        for i in range(2):
+            assert torch.allclose(  # type: ignore[attr-defined]
+                samples[i]["image"], new_samples[i]["image"]
+            )
+            assert samples[i]["crs"] == new_samples[i]["crs"]
+
     def test_concat_samples(self, samples: List[Dict[str, Any]]) -> None:
         sample = concat_samples(samples)
         assert sample["image"].size() == torch.Size([6])  # type: ignore[attr-defined]
@@ -500,7 +508,7 @@ def samples(self) -> List[Dict[str, Any]]:
             },
         ]
 
-    def test_stack_samples(self, samples: List[Dict[str, Any]]) -> None:
+    def test_stack_unbind_samples(self, samples: List[Dict[str, Any]]) -> None:
         sample = stack_samples(samples)
         assert sample["image"].size() == torch.Size(  # type: ignore[attr-defined]
             [1, 3]
@@ -515,6 +523,16 @@ def test_stack_samples(self, samples: List[Dict[str, Any]]) -> None:
         assert sample["crs1"] == [CRS.from_epsg(2000)]
         assert sample["crs2"] == [CRS.from_epsg(2001)]
 
+        new_samples = unbind_samples(sample)
+        assert torch.allclose(  # type: ignore[attr-defined]
+            samples[0]["image"], new_samples[0]["image"]
+        )
+        assert samples[0]["crs1"] == new_samples[0]["crs1"]
+        assert torch.allclose(  # type: ignore[attr-defined]
+            samples[1]["mask"], new_samples[0]["mask"]
+        )
+        assert samples[1]["crs2"] == new_samples[0]["crs2"]
+
     def test_concat_samples(self, samples: List[Dict[str, Any]]) -> None:
         sample = concat_samples(samples)
         assert sample["image"].size() == torch.Size([3])  # type: ignore[attr-defined]
diff --git a/torchgeo/datasets/utils.py b/torchgeo/datasets/utils.py
index 83f7b749144..091d6f16c37 100644
--- a/torchgeo/datasets/utils.py
+++ b/torchgeo/datasets/utils.py
@@ -448,6 +448,26 @@ def _list_dict_to_dict_list(samples: Iterable[Dict[Any, Any]]) -> Dict[Any, List
     return collated
 
 
+def _dict_list_to_list_dict(sample: Dict[Any, Sequence[Any]]) -> List[Dict[Any, Any]]:
+    """Convert a dictionary of lists to a list of dictionaries.
+
+    Args:
+        sample: a dictionary of lists
+
+    Returns:
+        a list of dictionaries
+
+    .. versionadded:: 0.2
+    """
+    uncollated: List[Dict[Any, Any]] = [
+        {} for _ in range(max(map(len, sample.values())))
+    ]
+    for key, values in sample.items():
+        for i, value in enumerate(values):
+            uncollated[i][key] = value
+    return uncollated
+
+
 def stack_samples(samples: Iterable[Dict[Any, Any]]) -> Dict[Any, Any]:
     """Stack a list of samples along a new axis.
 
@@ -532,15 +552,10 @@ def unbind_samples(sample: Dict[Any, Sequence[Any]]) -> List[Dict[Any, Any]]:
 
     .. versionadded:: 0.2
     """
-    uncollated: List[Dict[Any, Any]] = [{}] * max(map(len, sample.values()))
     for key, values in sample.items():
         if isinstance(values, Tensor):
-            for i, value in enumerate(torch.unbind(values)):
-                uncollated[i][key] = value
-        else:
-            for i, value in enumerate(values):
-                uncollated[i][key] = value
-    return uncollated
+            sample[key] = torch.unbind(values)
+    return _dict_list_to_list_dict(sample)
 
 
 def rasterio_loader(path: str) -> np.ndarray:  # type: ignore[type-arg]