Add consistent author and copyright info to all tutorials

docs/tutorials/basic_usage.rst

@@ -4,7 +4,7 @@ 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
+* `Spectral Indices <indices.ipynb>`_: Visualizing and appending spectral indices
 * `Pretrained Weights <pretrained_weights.ipynb>`_: Models and pretrained weights
 * `Lightning Trainers <trainers.ipynb>`_: PyTorch Lightning data modules and trainers
 

docs/tutorials/contribute_datamodule.ipynb

@@ -1,5 +1,15 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "# Licensed under the MIT License."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

docs/tutorials/custom_raster_dataset.ipynb

@@ -1,14 +1,15 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "id": "iiqWbXISOEAQ"
    },
+   "outputs": [],
    "source": [
-    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-    "\n",
-    "Licensed under the MIT License."
+    "# Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "# Licensed under the MIT License."
    ]
   },
   {
@@ -19,6 +20,8 @@
    "source": [
     "# Custom Raster Datasets\n",
     "\n",
+    "_Written by: Ritwik Gupta_\n",
+    "\n",
     "In this tutorial, we'll describe how to write a custom dataset in TorchGeo. There are many types of datasets that you may encounter, from image data, to segmentation masks, to point labels. We'll focus on the most common type of dataset: a raster file containing an image or mask. Let's get started!"
    ]
   },
@@ -562,9 +565,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

docs/tutorials/earth_surface_water.ipynb

@@ -1,5 +1,15 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "# Licensed under the MIT License."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

docs/tutorials/indices.ipynb

@@ -1,14 +1,15 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "id": "DYndcZst_kdr"
    },
+   "outputs": [],
    "source": [
-    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-    "\n",
-    "Licensed under the MIT License."
+    "# Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "# Licensed under the MIT License."
    ]
   },
   {
@@ -17,7 +18,9 @@
     "id": "ZKIkyiLScf9P"
    },
    "source": [
-    "# Indices"
+    "# Spectral Indices\n",
+    "\n",
+    "_Written by: Isaac A. Corley_"
    ]
   },
   {
@@ -374,9 +377,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }

docs/tutorials/trainers.ipynb

@@ -1,15 +1,16 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
-   "id": "b13c2251",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16421d50-8d7a-4972-b06f-160fd890cc86",
    "metadata": {
     "id": "b13c2251"
    },
+   "outputs": [],
    "source": [
-    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-    "\n",
-    "Licensed under the MIT License."
+    "# Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "# Licensed under the MIT License."
    ]
   },
   {
@@ -21,6 +22,8 @@
    "source": [
     "# Lightning Trainers\n",
     "\n",
+    "_Written by: Caleb Robinson_\n",
+    "\n",
     "In this tutorial, we demonstrate TorchGeo trainers to train and test a model. We will use the [EuroSAT](https://torchgeo.readthedocs.io/en/stable/api/datasets.html#eurosat) dataset throughout this tutorial. Specifically, a subset containing only 100 images. We will train models to predict land cover classes.\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."
@@ -328,7 +331,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.13.0"
   }
  },
  "nbformat": 4,