Merge pull request #2367 from Esri/jy-rvw-coast

Update urls in hyperlinks and change format of references to other api doc to be relative

Author: jyaistMap

samples/04_gis_analysts_data_scientists/coastline_classification_using_feature_classifier.ipynb

@@ -46,7 +46,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We have already explored how to [extract coastlines using Landsat-8 multispectral imagery and band ratio technique](https://developers.arcgis.com/python/sample-notebooks/coastline-extraction-usa-landsat8-multispectral-imagery/). Now, the next step is to classify these coastlines into **multiple categories** based on their characteristics. To achieve this, we will **train a deep learning model** that can classify each coastline segment into one of the categories shown below:\n",
+    "We have already explored how to [extract coastlines using Landsat-8 multispectral imagery and band ratio technique](/python/latest/samples/coastline-extraction-usa-landsat8-multispectral-imagery/). Now, the next step is to classify these coastlines into **multiple categories** based on their characteristics. To achieve this, we will **train a deep learning model** that can classify each coastline segment into one of the categories shown below:\n",
     "\n",
     "<center><img src=\"data:image/PNG; base64, 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\"> </center>\n",
     "\n",
@@ -189,7 +189,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using [ArcGIS Maritime](https://desktop.arcgis.com/en/arcmap/latest/extensions/maritime-charting/what-is-the-arcgis-for-maritime-charting-.htm ), we imported [NOAA’s Electronic Navigational Charts](https://www.charts.noaa.gov/ENCs/ENCs.shtml). The maritime data in these charts contain the coastline feature class with the category of coastline details. The Sentinel 2 imagery has been downloaded from the [Copernicus Open Access Hub](https://scihub.copernicus.eu/dhus/).\n",
+    "Using [ArcGIS Maritime](https://pro.arcgis.com/en/pro-app/latest/help/production/maritime/get-started-with-maritime-charting.htm), we imported [NOAA’s Electronic Navigational Charts](https://www.charts.noaa.gov/ENCs/ENCs.shtml). The maritime data in these charts contain the coastline feature class with the category of coastline details. The Sentinel 2 imagery has been downloaded from the [Copernicus Open Access Hub](https://atlas.co/data-sources/copernicus-open-access-hub/).\n",
     "\n",
     "Before exporting the data, we will create a grid pattern—illustrated in Figure 2—along the coastline to serve as a feature class during export. To do this, we will use the <b>Generate Rectangles Along Lines</b> tool. The required parameters for this tool are as follows:\n",
     "\n",
@@ -433,7 +433,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "ArcGIS's `arcgis.learn` module allows you to build a Feature Classifier model that can classify individual features based on training data. To understand the inner workings and potential applications of this model, refer to the official guide: [\"How feature classifier works?\"](https://developers.arcgis.com/python/guide/how-feature-categorization-works/)."
+    "ArcGIS's `arcgis.learn` module allows you to build a Feature Classifier model that can classify individual features based on training data. To understand the inner workings and potential applications of this model, refer to the official guide: [\"How feature classifier works?\"](/guide/how-feature-categorization-works/)."
    ]
   },
   {
@@ -1213,9 +1213,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:conda-pydl2]",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda-env-conda-pydl2-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1227,7 +1227,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,