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 facilitate 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. "
    ]
   },
   {
@@ -49,7 +49,7 @@
     "\n",
     "There are many types of remote sensing datasets. [Satellite-Image-Deep-Learning](https://github.com/satellite-image-deep-learning/datasets) maintains a list of many of these datasets, as well as links to other similar curated lists.\n",
     "\n",
-    "Two aspects that will make it a lot easier to add the dataset are whether or not the dataset can be easily downloaded and whether or the dataset comes with a Github repository and publication that outlines how the authors intend the dataset to be used. These are not necessariy criteria, and sometimes it might be even more worthwhile to add a dataset without an existing code base, precisely because the marginal contribution to the community might be greater since a use of the dataset does not necessitate writing the loading implementation from scratch."
+    "Two aspects that will make it a lot easier to add the dataset are whether or not the dataset can be easily downloaded and whether or the dataset comes with a Github repository and publication that outlines how the authors intend the dataset to be used. These are not necessary criteria, and sometimes it might be even more worthwhile to add a dataset without an existing code base, precisely because the marginal contribution to the community might be greater since a use of the dataset does not necessitate writing the loading implementation from scratch."
    ]
   },
   {

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)"
    ]

docs/tutorials/geospatial.ipynb

@@ -22,7 +22,7 @@
     "\n",
     "_Written by: Adam J. Stewart_\n",
     "\n",
-    "In this tutorial, we introduce the challenges of working with geospatial data, especially remote sensing imagery. This is not meant to discourage practicioners, but to elucidate why existing computer vision domain libraries like torchvision are insufficient for working with multispectral satellite imagery."
+    "In this tutorial, we introduce the challenges of working with geospatial data, especially remote sensing imagery. This is not meant to discourage practitioners, but to elucidate why existing computer vision domain libraries like torchvision are insufficient for working with multispectral satellite imagery."
    ]
   },
   {
@@ -110,7 +110,7 @@
     "\n",
     "Passive sensors (ones that do not emit light) are limited by daylight hours and cloud-free conditions. Active sensors such as radar emit polarized microwave pulses and measure the time it takes for the signal to reflect or scatter off of objects. This allows radar satellites to operate at night and in adverse weather conditions. The images captured by these sensors are stored as complex numbers, with a real (amplitude) and imaginary (phase) component, making it difficult to integrate them into machine learning pipelines.\n",
     "\n",
-    "Radar is commonly used in meteorology (Doppler radar) and geophysics (ground penetrating radar). By attaching a radar antenna to a moving satellite, a larger effective aperature is created, increasing the spatial resolution of the captured image. This technique is known as synthetic aperature radar (SAR), and has many common applications in geodesy, flood mapping, and glaciology. Finally, by comparing the phases of multiple SAR snapshots of a single location at different times, we can analyze minute changes in surface elevation, in a technique known as Interferometric Synthetic Aperature Radar (InSAR). Below, we see an interferogram of earthquake deformation:\n",
+    "Radar is commonly used in meteorology (Doppler radar) and geophysics (ground penetrating radar). By attaching a radar antenna to a moving satellite, a larger effective aperture is created, increasing the spatial resolution of the captured image. This technique is known as synthetic aperture radar (SAR), and has many common applications in geodesy, flood mapping, and glaciology. Finally, by comparing the phases of multiple SAR snapshots of a single location at different times, we can analyze minute changes in surface elevation, in a technique known as Interferometric Synthetic Aperture Radar (InSAR). Below, we see an interferogram of earthquake deformation:\n",
     "\n",
     "<center>\n",
     "<img src=\"https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2004/07/envisat_wsm_im_insar_image_of_bam/9998399-2-eng-GB/Envisat_WSM_IM_InSAR_image_of_Bam_pillars.jpg\" width=\"800\">\n",