made changes based on review

Author: mayank2

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

@@ -6,7 +6,7 @@
     "id": "VTzz2ClM2sPe"
    },
    "source": [
-    "# Detecting airplanes on satellite imagery using deep learning"
+    "# Detecting airplanes in satellite imagery using deep learning"
    ]
   },
   {
@@ -28,8 +28,8 @@
     "* [Necessary Imports](#2)\n",
     "* [Visualize training data](#3)\n",
     "* [Train a model](#4)\n",
-    "* [Detect and visualize airplanes on validation set](#5)\n",
-    "* [Model inference on ArcGIS Pro](#6)\n",
+    "* [Detect and visualize airplanes in validation set](#5)\n",
+    "* [Model inference in ArcGIS Pro](#6)\n",
     "* [Conclusion](#7)\n",
     "* [References](#8)"
    ]
@@ -49,7 +49,7 @@
     "id": "kCPaThDs2sPq"
    },
    "source": [
-    "The dataset used in this notebook is downloaded from [cosmiQ works](https://www.cosmiqworks.org/rareplanes/). We are going to use real training data which contains 5815 number of training images with dimensions 512x512 with 30cm resolution. Tiled images have been put into images folder. The annotations have been converted to \"PASCAL_VOC_rectangles\" format and have been put into labels folder. The dataset in the required format can be downloaded from [here](https://geosaurus.maps.arcgis.com/home/item.html?id=9982c9a749f94f7797e82358a90527c1)."
+    "The dataset we used in this notebook can be downloaded from [cosmiQ works](https://www.cosmiqworks.org/rareplanes/). We are going to use real training data that contains 5,815 training images. The images have dimensions of 512x512 and a resolution of 30cm. Tiled images have also been put into an images folder, and the annotations have been converted to the \"PASCAL_VOC_rectangles\" format and have been put into a labels folder. The dataset can be downloaded in the necessary format [here](https://geosaurus.maps.arcgis.com/home/item.html?id=9982c9a749f94f7797e82358a90527c1)."
    ]
   },
   {
@@ -67,7 +67,7 @@
     "id": "VRo2yMFj4FBT"
    },
    "source": [
-    "The goal of this notebook is to show how we can use FasterRCNN model to detect airplanes in satellite imagery. Airplane detection is desireable in various industries as we can locate the airports globally on a very large scale and can also see how busy the airport was at a particular time."
+    "The goal of this notebook is to demonstrate how we can use the FasterRCNN model to detect airplanes in satellite imagery. Airplane detection is a valuable process across multiple industries, as it allows for the identification and monitoring of airports at a global scale."
    ]
   },
   {
@@ -97,11 +97,11 @@
     "id": "zNpEBlBg2sPs"
    },
    "source": [
-    "We will now use the prepare_data() function to apply various types of transformations and augmentations on the training data. These augmentations enable us to train a better model with limited data and also prevent the model from overfitting.\n",
+    "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",
-    "Since we only need to detect only one class i.e airplanes and not seven different types of airplanes, we will map them to one class using `class_mapping`.\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",
     "\n",
-    "This function returns a data object which can be fed into a model for training."
+    "This function returns a data object that can be fed into a model for training."
    ]
   },
   {
@@ -191,7 +191,7 @@
     "id": "CtdZDnNp2sPw"
    },
    "source": [
-    "We can just simply call model.fit() method and pass number of epochs to it. Model will train after finding the optimum learning rate itself."
+    "Next, we can simply call the model.fit() method and pass a number of epochs to it. The model will then begin to train after finding the optimal learning rate itself."
    ]
   },
   {
@@ -342,7 +342,7 @@
     "id": "BHgFpzZU2sPx"
    },
    "source": [
-    "## Detect and visualize airplanes on validation set <a class=\"anchor\" id=\"5\"></a>"
+    "## Detect and visualize airplanes on the validation set <a class=\"anchor\" id=\"5\"></a>"
    ]
   },
   {
@@ -351,7 +351,7 @@
     "id": "nLXHu_2f2sPx"
    },
    "source": [
-    "Now we have the model, let's look at how the model performs on unseen data."
+    "Now that we have the trained model, we will examine how the model performs on data it has not yet seen."
    ]
   },
   {
@@ -394,7 +394,7 @@
     "id": "HtgSElk62sPy"
    },
    "source": [
-    "Let's save the model for further training or inference later. The model should be saved into a models folder in your data folder by default."
+    "We will now save the model for further training or later inferences. By default, the model should save into a models folder in your data folder."
    ]
   },
   {
@@ -426,7 +426,7 @@
     "id": "bIKlNH2f2sPz"
    },
    "source": [
-    "## Model inference on ArcGIS Pro <a class=\"anchor\" id=\"6\"></a>"
+    "## Model inferencing in ArcGIS Pro <a class=\"anchor\" id=\"6\"></a>"
    ]
   },
   {
@@ -435,7 +435,7 @@
     "id": "qHnBzpcN2sPz"
    },
    "source": [
-    "We will use saved model to detect airplanes using Detect Objects Using Deep Learning tool available in both ArcGIS Pro and ArcGIS Image Server. For this sample we will deploy the model on Amsterdam airport region.\n",
+    "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",
     "- Output Detected Objects: Detected_airplanes\n",
@@ -460,7 +460,7 @@
     "id": "81XHHzwZ2sP8"
    },
    "source": [
-    "Detect Objects Using Deep Learning returns a vector layer with detected airplanes of a region."
+    "The Detect Objects Using Deep Learning tool will return a vector layer with the airplanes detected in the chosen region, as seen below."
    ]
   },
   {