minor changes

Author: mayank2

samples/04_gis_analysts_data_scientists/detecting-airplanes-on-satellite-imagery-using-deep-learning.ipynb

@@ -24,6 +24,7 @@
     "id": "0uYpeRvR2sPp"
    },
    "source": [
+    "* [Prerequisites](#9)\n",
     "* [Introduction](#1)\n",
     "* [Necessary Imports](#2)\n",
     "* [Visualize training data](#3)\n",
@@ -40,7 +41,7 @@
     "id": "8Fc-PCkB2sPq"
    },
    "source": [
-    "## Prerequisites"
+    "## Prerequisites <a class=\"anchor\" id=\"9\"></a>"
    ]
   },
   {
@@ -99,7 +100,7 @@
    "source": [
     "We will now use the `prepare_data()` function to apply various types of transformations and augmentations to the training data. These augmentations enable us to train a better model with limited data, as well as better prevent the overfitting of the model.\n",
     "\n",
-    "As we are only interested in tracking all planes, rather than individual classes of planes, we will map the planes to one class using`class_mapping`.\n",
+    "This dataset contains multiple classes of planes, however, we are only interested in detecting planes and not classifying their categories. So, we will map the planes to one class using `class_mapping`.\n",
     "\n",
     "This function returns a data object that can be fed into a model for training."
    ]
@@ -437,7 +438,7 @@
    "source": [
     "Next, we will use the saved model to detect airplanes using the Detect Objects Using Deep Learning tool available in both ArcGIS Pro and ArcGIS Image Server. For this sample, we will deploy the model on the Amsterdam airport region.\n",
     "\n",
-    "- Input Raster: World Imagery\n",
+    "- Input Raster: Imagery\n",
     "- Output Detected Objects: Detected_airplanes\n",
     "- Model Definition: airplane_15ep.emd\n",
     "- Extent: Extent of the region where model will run.\n",