diff --git a/tests/data/reforestree/data.py b/tests/data/reforestree/data.py
index 6479535cacb..41d210a31b8 100644
--- a/tests/data/reforestree/data.py
+++ b/tests/data/reforestree/data.py
@@ -51,7 +51,6 @@ def create_img(path: str) -> None:
 if __name__ == "__main__":
     data_root = "data"
     # remove old data
-    # remove old data
     if os.path.isdir(data_root):
         shutil.rmtree(data_root)
     else:
diff --git a/torchgeo/datasets/reforestree.py b/torchgeo/datasets/reforestree.py
index a94ea7620dd..6121ca4af29 100644
--- a/torchgeo/datasets/reforestree.py
+++ b/torchgeo/datasets/reforestree.py
@@ -21,7 +21,7 @@
 class ReforesTree(VisionDataset):
     """ReforesTree dataset.
 
-    The `ReforesTree <https://github.com/gyrrei/ReforesTree>`_
+    The `ReforesTree <https://github.com/gyrrei/ReforesTree>`__
     dataset contains drone imagery that can be used for tree crown detection,
     tree species classification and Aboveground Biomass (AGB) estimation.
 
@@ -48,21 +48,13 @@ class ReforesTree(VisionDataset):
 
     If you use this dataset in your research, please cite the following paper:
 
-    * Reiersen, Gyri, et al. "ReforesTree: A Dataset for Estimating Tropical
-      Forest Carbon Stock with Deep Learning and Aerial Imagery."
-      arXiv preprint arXiv:2201.11192 (2022).
+    * https://arxiv.org/abs/2201.11192
 
     .. versionadded:: 0.3
     """
 
     classes = ["other", "banana", "cacao", "citrus", "fruit", "timber"]
-    url = (
-        "https://uc62e9acc213a8f56919a7a7add8.dl.dropboxusercontent.com/"
-        "zip_download_get/BKn4_obEfo0iR6UUpOqBMp9K0ohkldQtJTUN802va4SgJvd"
-        "OsN36ap5fcbbxFLbhPMHoI1oJBrkjwJWyYkcBE-SxCeFPTZ0a-gOBozXL7CfaKg?"
-        "_download_id=600502027618175772326304665489490007078769290252973"
-        "08895167120694292&_notify_domain=www.dropbox.com&dl=1"
-    )
+    url = "https://uc62e9acc213a8f56919a7a7add8.dl.dropboxusercontent.com/zip_download_get/BKn4_obEfo0iR6UUpOqBMp9K0ohkldQtJTUN802va4SgJvdOsN36ap5fcbbxFLbhPMHoI1oJBrkjwJWyYkcBE-SxCeFPTZ0a-gOBozXL7CfaKg?_download_id=60050202761817577232630466548949000707876929025297308895167120694292&_notify_domain=www.dropbox.com&dl=1"
 
     md5 = "f6a4a1d8207aeaa5fbab7b21b683a302"
     zipfilename = "data.zip"
@@ -161,7 +153,7 @@ def _load_image(self, path: str) -> Tensor:
         """
         with Image.open(path) as img:
             array: "np.typing.NDArray[np.int_]" = np.array(img.convert("RGB"))
-            tensor: Tensor = torch.from_numpy(array)
+            tensor = torch.from_numpy(array)
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))
             return tensor
@@ -177,13 +169,11 @@ def _load_target(self, filepath: str) -> Tuple[Tensor, ...]:
         """
         tile_df = self.annot_df[self.annot_df["img_path"] == os.path.basename(filepath)]
 
-        boxes: Tensor = torch.Tensor(
-            tile_df[["xmin", "ymin", "xmax", "ymax"]].values.tolist()
-        )
-        labels: Tensor = torch.Tensor(
+        boxes = torch.Tensor(tile_df[["xmin", "ymin", "xmax", "ymax"]].values.tolist())
+        labels = torch.Tensor(
             [self.class2idx[label] for label in tile_df["group"].tolist()]
         )
-        agb: Tensor = torch.Tensor(tile_df["AGB"].tolist())
+        agb = torch.Tensor(tile_df["AGB"].tolist())
 
         return boxes, labels, agb
 
@@ -192,6 +182,9 @@ def _verify(self) -> None:
 
         Returns:
             True if the dataset directories are found, else False
+
+        Raises:
+            Runtime Error if dataset is not found in root or is corrupted
         """
         filepaths = [os.path.join(self.root, dir) for dir in ["tiles", "mapping"]]
         if all([os.path.exists(filepath) for filepath in filepaths]):
@@ -207,8 +200,8 @@ def _verify(self) -> None:
         # Check if the user requested to download the dataset
         if not self.download:
             raise RuntimeError(
-                "Dataset not found in `root` directory, either specify a different"
-                + " `root` directory or manually download "
+                f"Dataset not found in {self.root} directory, either specify a"
+                + " different `root` directory or manually download "
                 + "the dataset to this directory."
             )