diff --git a/docs/index.rst b/docs/index.rst
index 561a496d67a..02ba03a8cc5 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -12,6 +12,15 @@ torchgeo
    user/glossary
    user/alternatives
 
+.. toctree::
+   :maxdepth: 2
+   :caption: Tutorials
+
+   tutorials/getting_started
+   tutorials/basic_usage
+   tutorials/case_studies
+   tutorials/customization
+
 .. toctree::
    :maxdepth: 2
    :caption: Package Reference
@@ -24,23 +33,6 @@ torchgeo
    api/trainers
    api/transforms
 
-.. toctree::
-   :maxdepth: 1
-   :caption: Tutorials
-
-   tutorials/getting_started
-   tutorials/pytorch
-   tutorials/geospatial
-   tutorials/torchgeo
-   tutorials/contribute_non_geo_dataset
-   tutorials/custom_raster_dataset
-   tutorials/contribute_datamodule
-   tutorials/transforms
-   tutorials/indices
-   tutorials/trainers
-   tutorials/pretrained_weights
-   tutorials/earth_surface_water
-
 .. toctree::
    :maxdepth: 1
    :caption: PyTorch Libraries
diff --git a/docs/tutorials/basic_usage.rst b/docs/tutorials/basic_usage.rst
new file mode 100644
index 00000000000..10fcb97b42d
--- /dev/null
+++ b/docs/tutorials/basic_usage.rst
@@ -0,0 +1,18 @@
+Basic Usage
+===========
+
+The following tutorials introduce the basic concepts and components of TorchGeo:
+
+* `Transforms <transforms.ipynb>`_: Preprocessing and data augmentation transforms for geospatial data
+* `Indices <indices.ipynb>`_: Spectral indices
+* `Pretrained Weights <pretrained_weights.ipynb>`_: Models and pretrained weights
+* `Lightning Trainers <trainers.ipynb>`_: PyTorch Lightning data modules and trainers
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+
+   transforms
+   indices
+   pretrained_weights
+   trainers
diff --git a/docs/tutorials/case_studies.rst b/docs/tutorials/case_studies.rst
new file mode 100644
index 00000000000..aee09a94265
--- /dev/null
+++ b/docs/tutorials/case_studies.rst
@@ -0,0 +1,14 @@
+Case Studies
+============
+
+The following case studies present end-to-end workflows for common use cases of geospatial machine learning:
+
+* `Earth Surface Water <earth_surface_water.ipynb>`_: A workflow for mapping surface water, including lakes and rivers
+
+Do you have a use case that is missing from this list? Please open a pull request to add tutorials for your own use cases.
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+
+   earth_surface_water
diff --git a/docs/tutorials/contribute_non_geo_dataset.ipynb b/docs/tutorials/contribute_non_geo_dataset.ipynb
index 655deb1c8d7..4a702222daf 100644
--- a/docs/tutorials/contribute_non_geo_dataset.ipynb
+++ b/docs/tutorials/contribute_non_geo_dataset.ipynb
@@ -259,7 +259,11 @@
     "# Create dummy data for all splits and filenames\n",
     "for split in splits:\n",
     "    for filename in image_file_names:\n",
-    "        create_input_image(split, filename)\n",
+    "        create_input_image(\n",
+    "            os.path.join(root_dir, 'image', split, filename),\n",
+    "            (IMG_SIZE, IMG_SIZE),\n",
+    "            range(2**16),\n",
+    "        )\n",
     "        create_target_images(split, filename.replace('_', '_target_'))\n",
     "\n",
     "# Zip directory\n",
diff --git a/docs/tutorials/customization.rst b/docs/tutorials/customization.rst
new file mode 100644
index 00000000000..07b0870607b
--- /dev/null
+++ b/docs/tutorials/customization.rst
@@ -0,0 +1,20 @@
+Customization
+=============
+
+Is TorchGeo missing a dataset or model you need? Would you like to modify the default augmentations for a data module or extend a builtin trainer?
+
+The following tutorials will teach you how to customize TorchGeo to meet your needs:
+
+* `Custom Non-Geospatial Datasets <contribute_non_geo_dataset.ipynb>`_: How to create and contribute a new NonGeoDataset
+* `Custom Raster Datasets <custom_raster_dataset.ipynb>`_: How to create a new RasterDataset
+* `Custom Data Module <contribute_datamodule.ipynb>`_: How to create and contribute a new DataModule
+
+TorchGeo is a community-driven open source library. If there is a feature missing that you would like to add, please open a pull request to add it. See the ref:`contributing` guidelines to get started.
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+
+   contribute_non_geo_dataset
+   custom_raster_dataset
+   contribute_datamodule
diff --git a/docs/tutorials/getting_started.ipynb b/docs/tutorials/getting_started.ipynb
deleted file mode 100644
index 9bdb0eed11c..00000000000
--- a/docs/tutorials/getting_started.ipynb
+++ /dev/null
@@ -1,324 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "35303546",
-   "metadata": {},
-   "source": [
-    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-    "\n",
-    "Licensed under the MIT License."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9478ed9a",
-   "metadata": {
-    "id": "NdrXRgjU7Zih"
-   },
-   "source": [
-    "# Getting Started\n",
-    "\n",
-    "In this tutorial, we demonstrate some of the basic features of TorchGeo and show how easy it is to use if you're already familiar with other PyTorch domain libraries like torchvision.\n",
-    "\n",
-    "It's recommended to run this notebook on Google Colab if you don't have your own GPU. Click the \"Open in Colab\" button above to get started."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "34f10e9f",
-   "metadata": {
-    "id": "lCqHTGRYBZcz"
-   },
-   "source": [
-    "## Setup\n",
-    "\n",
-    "First, we install TorchGeo."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "019092f0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install torchgeo planetary_computer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4db9f791",
-   "metadata": {
-    "id": "dV0NLHfGBMWl"
-   },
-   "source": [
-    "## Imports\n",
-    "\n",
-    "Next, we import TorchGeo and any other libraries we need."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3d92b0f1",
-   "metadata": {
-    "id": "entire-albania"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import tempfile\n",
-    "\n",
-    "import planetary_computer\n",
-    "from torch.utils.data import DataLoader\n",
-    "\n",
-    "from torchgeo.datasets import NAIP, ChesapeakeDE, stack_samples\n",
-    "from torchgeo.datasets.utils import download_url\n",
-    "from torchgeo.samplers import RandomGeoSampler"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7f26e4b8",
-   "metadata": {
-    "id": "5rLknZxrBEMz"
-   },
-   "source": [
-    "## Datasets\n",
-    "\n",
-    "For this tutorial, we'll be using imagery from the [National Agriculture Imagery Program (NAIP)](https://catalog.data.gov/dataset/national-agriculture-imagery-program-naip) and labels from the [Chesapeake Bay High-Resolution Land Cover Project](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/). First, we manually download a few NAIP tiles and create a PyTorch Dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4a39af46",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 232,
-     "referenced_widgets": [
-      "d00a2177bf4b4b8191bfc8796f0e749f",
-      "17d6b81aec50455989276b595457cc7f",
-      "06ccd130058b432dbfa025c102eaeb27",
-      "6bc5b9872b574cb5aa6ebd1d44e7a71f",
-      "f7746f028f874a85b6101185fc9a8efc",
-      "f7ef78d6f87a4a2685788e395525fa7c",
-      "5b2450e316e64b4ba432c78b63275124",
-      "d3bbd6112f144c77bc68e5f2a7a355ff",
-      "a0300b1252cd4da5a798b55c15f8f5fd",
-      "793c2851b6464b398f7b4d2f2f509722",
-      "8dd61c8479d74c95a55de147e04446b3",
-      "b57d15e6c32b4fff8994ae67320972f6",
-      "9a34f8907a264232adf6b0d0543461dd",
-      "e680eda3c84c440083e2959f04431bea",
-      "a073e33fd9ae4125822fc17971233770",
-      "87faaa32454a42939d3bd405e726228c",
-      "b3d4c9c99bec4e69a199e45920d52ce4",
-      "a215f3310ea543d1a8991f57ec824872",
-      "569f60397fd6440d825e8afb83b4e1ae",
-      "b7f604d2ba4e4328a451725973fa755f",
-      "737fa148dfae49a18cc0eabbe05f2d0f",
-      "0b6613adbcc74165a9d9f74988af366e",
-      "b25f274c737d4212b3ffeedb2372ba22",
-      "ef0fc75ff5044171be942a6b3ba0c2da",
-      "612d84013a6e4890a48eb229f6431233",
-      "9a689285370646ab800155432ea042a5",
-      "014ed48a23234e8b81dd7ac4dbf95817",
-      "93c536a27b024728a00486b1f68b4dde",
-      "8a8538a91a74439b81e3f7c6516763e3",
-      "caf540562b484594bab8d6210dd7c2c1",
-      "99cd2e65fb104380953745f2e0a93fac",
-      "c5b818707bb64c5a865236a46399cea2",
-      "54f5db9555c44efa9370cbb7ab58e142",
-      "1d83b20dbb9c4c6a9d5c100fe4770ba4",
-      "c51b2400ca9442a9a9e0712d5778cd9a",
-      "bd2e44a8eb1a4c19a32da5a1edd647d1",
-      "0f9feea4b8344a7f8054c9417150825e",
-      "31acb7a1ca8940078e1aacd72e547f47",
-      "0d0ca8d64d3e4c2f88d87342808dd677",
-      "54402c5f8df34b83b95c94104b26e2c6",
-      "910b98584fa74bb5ad308fe770f5b40e",
-      "b2dce834ee044d69858389178b493a2b",
-      "237f2e31bcfe476baafae8d922877e07",
-      "43ac7d95481b4ea3866feef6ace2f043"
-     ]
-    },
-    "id": "e3138ac3",
-    "outputId": "11589c46-eee6-455d-839b-390f2934d834"
-   },
-   "outputs": [],
-   "source": [
-    "naip_root = os.path.join(tempfile.gettempdir(), 'naip')\n",
-    "naip_url = (\n",
-    "    'https://naipeuwest.blob.core.windows.net/naip/v002/de/2018/de_060cm_2018/38075/'\n",
-    ")\n",
-    "tiles = [\n",
-    "    'm_3807511_ne_18_060_20181104.tif',\n",
-    "    'm_3807511_se_18_060_20181104.tif',\n",
-    "    'm_3807512_nw_18_060_20180815.tif',\n",
-    "    'm_3807512_sw_18_060_20180815.tif',\n",
-    "]\n",
-    "for tile in tiles:\n",
-    "    url = planetary_computer.sign(naip_url + tile)\n",
-    "    download_url(url, naip_root, filename=tile)\n",
-    "\n",
-    "naip = NAIP(naip_root)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e25bad40",
-   "metadata": {
-    "id": "HQVji2B22Qfu"
-   },
-   "source": [
-    "Next, we tell TorchGeo to automatically download the corresponding Chesapeake labels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "689bb2b0",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "2Ah34KAw2biY",
-    "outputId": "03b7bdf0-78c1-4a13-ac56-59de740d7f59"
-   },
-   "outputs": [],
-   "source": [
-    "chesapeake_root = os.path.join(tempfile.gettempdir(), 'chesapeake')\n",
-    "os.makedirs(chesapeake_root, exist_ok=True)\n",
-    "chesapeake = ChesapeakeDE(chesapeake_root, crs=naip.crs, res=naip.res, download=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "56f2d78b",
-   "metadata": {
-    "id": "OWUhlfpD22IX"
-   },
-   "source": [
-    "Finally, we create an IntersectionDataset so that we can automatically sample from both GeoDatasets simultaneously."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "daefbc4d",
-   "metadata": {
-    "id": "WXxy8F8l2-aC"
-   },
-   "outputs": [],
-   "source": [
-    "dataset = naip & chesapeake"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ded44652",
-   "metadata": {
-    "id": "yF_R54Yf3EUd"
-   },
-   "source": [
-    "## Sampler\n",
-    "\n",
-    "Unlike typical PyTorch Datasets, TorchGeo GeoDatasets are indexed using lat/long/time bounding boxes. This requires us to use a custom GeoSampler instead of the default sampler/batch_sampler that comes with PyTorch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b8a0d99c",
-   "metadata": {
-    "id": "RLczuU293itT"
-   },
-   "outputs": [],
-   "source": [
-    "sampler = RandomGeoSampler(dataset, size=1000, length=10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b8c1c52",
-   "metadata": {
-    "id": "OWa-mmYd8S6K"
-   },
-   "source": [
-    "## DataLoader\n",
-    "\n",
-    "Now that we have a Dataset and Sampler, we can combine these into a single DataLoader."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "96faa142",
-   "metadata": {
-    "id": "jfx-9ZmU8ZTc"
-   },
-   "outputs": [],
-   "source": [
-    "dataloader = DataLoader(dataset, sampler=sampler, collate_fn=stack_samples)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "64ae63f7",
-   "metadata": {
-    "id": "HZIfqqW58oZe"
-   },
-   "source": [
-    "## Training\n",
-    "\n",
-    "Other than that, the rest of the training pipeline is the same as it is for torchvision."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8a2b44f8",
-   "metadata": {
-    "id": "7sGmNvBy8uIg"
-   },
-   "outputs": [],
-   "source": [
-    "for sample in dataloader:\n",
-    "    image = sample['image']\n",
-    "    target = sample['mask']"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "getting_started.ipynb",
-   "provenance": []
-  },
-  "execution": {
-   "timeout": 1200
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/tutorials/getting_started.rst b/docs/tutorials/getting_started.rst
new file mode 100644
index 00000000000..8049a95a20f
--- /dev/null
+++ b/docs/tutorials/getting_started.rst
@@ -0,0 +1,18 @@
+Getting Started
+===============
+
+New to deep learning or remote sensing? First time using PyTorch or TorchGeo? You've come to the right place.
+
+The following tutorials will teach you enough to get started:
+
+* `Introduction to PyTorch <pytorch.ipynb>`_: A brief overview of deep learning with PyTorch
+* `Introduction to Geospatial Data <geospatial.ipynb>`_: A brief overview of the challenges of working with geospatial data
+* `Introduction to TorchGeo <torchgeo.ipynb>`_: A brief overview of the design of TorchGeo
+
+.. toctree::
+   :hidden:
+   :maxdepth: 1
+
+   pytorch
+   geospatial
+   torchgeo
diff --git a/docs/user/contributing.rst b/docs/user/contributing.rst
index ae3cdd41045..033fcecdea2 100644
--- a/docs/user/contributing.rst
+++ b/docs/user/contributing.rst
@@ -1,3 +1,5 @@
+.. _contributing:
+
 Contributing
 ============