Fix typos

docs/tutorials/contribute_datamodule.ipynb

@@ -23,7 +23,7 @@
    "source": [
     "_Written by: Nils Lehmann_\n",
     "\n",
-    "TorchGeo provides Lightning `DataModules` and trainers to faciliate easy and scalabel model training based on simple configuration files. Essentially, a `DataModule` implements the logic for splitting a dataset into train, validation and test splits for reproducability, wrapping them in PyTorch `DataLoaders` and apply augmentations to batches of data. This tutorial will outline a guide to adding a new datamodule to TorchGeo. It is often easy to do so alongside a new dataset and will make the dataset directly useable for a Lightning training and evaluation pipeline"
+    "TorchGeo provides Lightning `DataModules` and trainers to faciliate easy and scalabel model training based on simple configuration files. Essentially, a `DataModule` implements the logic for splitting a dataset into train, validation and test splits for reproducibility, wrapping them in PyTorch `DataLoaders` and apply augmentations to batches of data. This tutorial will outline a guide to adding a new datamodule to TorchGeo. It is often easy to do so alongside a new dataset and will make the dataset directly usable for a Lightning training and evaluation pipeline"
    ]
   },
   {

docs/tutorials/contribute_non_geo_dataset.ipynb

@@ -18,9 +18,9 @@
     "\n",
     "_Written by: Nils Lehmann_\n",
     "\n",
-    "Open-source datasets have significantly accelerated machine learning research. Geospatial machine learning datasets can be particularly complex to work with compared to more standard RGB-based vision datasets. To spare the community from having to repeatly implement data loading logic over and over, TorchGeo provides dozens of built-in datasets such that they can be downloaded and ready for use in a PyTorch framework with a single line of code. This tutorial will show how you can add a new non-geospatial dataset to this growing collection. \n",
+    "Open-source datasets have significantly accelerated machine learning research. Geospatial machine learning datasets can be particularly complex to work with compared to more standard RGB-based vision datasets. To spare the community from having to repeatedly implement data loading logic over and over, TorchGeo provides dozens of built-in datasets such that they can be downloaded and ready for use in a PyTorch framework with a single line of code. This tutorial will show how you can add a new non-geospatial dataset to this growing collection. \n",
     "\n",
-    "As a reminder, TorchGeo differentiates between two types of datasets: geospatial and non-geospatial datasets. Non-geospatial datasets are integer indexed, like the datasets one might be familar with from torchvision, while geospatial datasets are indexed via spatiotemporal bounding boxes. Non-geospatial datasets can still return geospatial and other metadata and should be specific to the remote sensing domain. "
+    "As a reminder, TorchGeo differentiates between two types of datasets: geospatial and non-geospatial datasets. Non-geospatial datasets are integer indexed, like the datasets one might be familiar with from torchvision, while geospatial datasets are indexed via spatiotemporal bounding boxes. Non-geospatial datasets can still return geospatial and other metadata and should be specific to the remote sensing domain. "
    ]
   },
   {

docs/tutorials/earth_surface_water.ipynb

@@ -244,7 +244,7 @@
     "id": "8RFrF3bTOSJn"
    },
    "source": [
-    "To create training patches that can be fed into a neural network from our dataset, we need to select samples of fixed sizes. TorchGeo has many samplers, but here we will use the `RandomGeoSampler` class. Basically, the sampler selects random bounding boxes of fixed size that belongs to the original image. Then, these bounding boxes are used in the `RasterDataset` to query the portion of the image we want. Here is an exmple using the previously created samplers."
+    "To create training patches that can be fed into a neural network from our dataset, we need to select samples of fixed sizes. TorchGeo has many samplers, but here we will use the `RandomGeoSampler` class. Basically, the sampler selects random bounding boxes of fixed size that belongs to the original image. Then, these bounding boxes are used in the `RasterDataset` to query the portion of the image we want. Here is an example using the previously created samplers."
    ]
   },
   {
@@ -497,7 +497,7 @@
     "        item.object for item in dset.index.intersection(dset.index.bounds, objects=True)\n",
     "    ]\n",
     "\n",
-    "    # Reseting statistics\n",
+    "    # Resetting statistics\n",
     "    accum_mean = 0\n",
     "    accum_std = 0\n",
     "\n",
@@ -506,7 +506,7 @@
     "        accum_mean += img.reshape((img.shape[0], -1)).mean(axis=1)\n",
     "        accum_std += img.reshape((img.shape[0], -1)).std(axis=1)\n",
     "\n",
-    "    # at the end, we shall have 2 vectors with lenght n=chnls\n",
+    "    # at the end, we shall have 2 vectors with length n=chnls\n",
     "    # we will average them considering the number of images\n",
     "    return accum_mean / len(files), accum_std / len(files)"
    ]