removed integer images from documentation

microsoft · Jan 17, 2024 · 635b383 · 635b383
1 parent 1fe44cb
commit 635b383
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diff --git a/docs/tutorials/custom_raster_dataset.ipynb b/docs/tutorials/custom_raster_dataset.ipynb
@@ -333,7 +333,7 @@
     "\n",
     "### `dtype`\n",
     "\n",
-    "Defaults to float32 for `is_image == True` and long for `is_image == False`. This is what is usually wanted for 99% of datasets but can be overridden for integer images (like some Digital Elevation Models) or pixel-wise regression masks (where the target should be float32). Uint16 and uint32 are automatically cast to int32 and int64, respectively, because numpy supports the former but torch does not.\n",
+    "Defaults to float32 for `is_image == True` and long for `is_image == False`. This is what is usually wanted for 99% of datasets but can be overridden for pixel-wise regression masks (where the target should be float32). Uint16 and uint32 are automatically cast to int32 and int64, respectively, because numpy supports the former but torch does not.\n",
     "\n",
     "### `separate_files`\n",
     "\n",

diff --git a/torchgeo/datasets/geo.py b/torchgeo/datasets/geo.py
@@ -370,11 +370,10 @@ def dtype(self) -> torch.dtype:
         """The dtype of the dataset (overrides the dtype of the data file via a cast).
 
         Defaults to float32 for is_image = True and long for is_image = False. This is
-        what we usually want for 99% of datasets but can be overridden for integer
-        images (like some imagery or Digital Elevation Models) or pixel-wise regression
-        masks (where it should be float32). Uint16 and uint32 are automatically cast to
-        int32 and int64, respectively, because numpy supports the former but torch does
-        not.
+        what we usually want for 99% of datasets but can be overridden for pixel-wise 
+        regression masks (where it should be float32). Uint16 and uint32 are
+        automatically cast to int32 and int64, respectively, because numpy supports
+        the former but torch does not.
 
         Returns:
             the dtype of the dataset