to be merged

Author: vbhv14

guide/14-deep-learning/point_cloud_classification_using_sqn.ipynb

@@ -30,7 +30,7 @@
    "source": [
     "SQN <a href=\"#References\">[1]</a> is a point cloud classification model available in the `arcgis.learn` module, designed to efficiently classify a vast amount of point clouds. Typically, LiDAR sensors use laser technology to survey the earth's surface, generating precise 3D coordinates (x, y, and z) that form point clouds. These points also have some additional information like 'GPS timestamps', 'intensity', and 'number of returns'. The intensity represents the returning strength from the laser pulse that scanned the area, and the number of returns shows how many times a given pulse returned. LiDAR data can also be fused with RGB (red, green, and blue) bands, derived from imagery taken simultaneously with the LiDAR survey. \n",
     "\n",
-    "Point cloud classification are based on the type of object that reflected the laser pulse. For example, a point that reflects off the ground is classified into the ground category. LiDAR points can be classified into different categories like buildings, trees, highways, water, etc. These different classes have numeric codes assigned to them."
+    "Point cloud classification is based on the type of object that reflected the laser pulse. For example, a point that reflects off the ground is classified into the ground category. LiDAR points can be classified into different categories like buildings, trees, highways, water, etc. These different classes have numeric codes assigned to them."
    ]
   },
   {
@@ -228,7 +228,7 @@
     "- `sub_sampling_ratio` which sets the sampling ratio of random sampling at each layer.\n",
     "\n",
     "\n",
-    "- `k_n` controls the number of K-nearest neighbor for a point.\n",
+    "- `k_n` controls the number of K-nearest neighbors for a point.\n",
     "\n",
     "\n",
     "A typical usage with respect to API looks like:\n",
@@ -298,9 +298,7 @@
     "\n",
     "After that, metapackage can be installed using the command below:\n",
     "\n",
-    "`conda install -c esri arcgis_learn python=3.9`\n",
-    "\n",
-    "_(Alternatively, 3.7 and 3.8 versions of `python` are also supported with the metapackage.)_"
+    "`conda install -c esri arcgis_learn python=3.9`"
    ]
   },
   {
@@ -456,7 +454,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.18"
   },
   "toc": {
    "base_numbering": 1,