Merge pull request #1103 from mohi9282/v191_release

Minor changes for v191 release

Author: Atma Mani

guide/05-working-with-the-spatially-enabled-dataframe/part1_introduction_to_sedf.ipynb

@@ -208,8 +208,8 @@
     "<div class=\"alert alert-info\">\n",
     "    <b>Note:</b> In the absence of <code>arcpy</code>, the ArcGIS API for Python looks for a <code>shapely</code> geometry engine. To allow for a seamless experience, both <a href=\"https://pypi.org/project/Shapely/\">Shapely</a> and <a href=\"https://pypi.org/project/Fiona/\">Fiona</a> packages must be present in your current conda environment. If these packages are not installed, you may install them using <code>conda</code> as follows:\n",
     "    \n",
-    "    conda install shapely\n",
-    "    conda install fiona\n",
+    "<code>conda install shapely\n",
+    "conda install fiona</code>\n",
     "</div>"
    ]
   },
@@ -1520,9 +1520,11 @@
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\">\n",
-    "    <b>Note:</b> Given the importance and popularity of <i>Spatially enabled DataFrame</i>, we are revisiting our documentation for this topic. Our goal is to enhance the existing documentation to showcase the various capabilities of Spatially enabled DataFrame in detail with even more examples this time. \n",
+    "    <b>Note:</b> Given the importance and popularity of <i>Spatially enabled DataFrame</i>, we are revisiting our documentation for this topic. Our goal is to enhance the existing documentation to showcase the various capabilities of Spatially enabled DataFrame in detail with even more examples this time.\n",
     "\n",
-    "   Creating quality documentation is time-consuming and exhaustive but we are committed to providing you with the best experience possible. With that in mind, we will be rolling out the revamped guides on this topic as different parts of a guide series (like the <a href=\"https://developers.arcgis.com/python/guide/part1-introduction-to-dataengineering/\">Data Engineering</a> or <a href=\"https://developers.arcgis.com/python/guide/part1-introduction-what-is-geometry/\">Geometry</a> guide series). This is \"part-1\" of the guide series for Spatially enabled DataFrame. You will continue to see the existing documentation as we revamp it to add new parts. Stay tuned for more on this topic.\n",
+    "<br></br>\n",
+    "\n",
+    "Creating quality documentation is time-consuming and exhaustive but we are committed to providing you with the best experience possible. With that in mind, we will be rolling out the revamped guides on this topic as different parts of a guide series (like the <a href=\"https://developers.arcgis.com/python/guide/part1-introduction-to-dataengineering/\">Data Engineering</a> or <a href=\"https://developers.arcgis.com/python/guide/part1-introduction-what-is-geometry/\">Geometry</a> guide series). This is \"part-1\" of the guide series for Spatially enabled DataFrame. You will continue to see the existing documentation as we revamp it to add new parts. Stay tuned for more on this topic.\n",
     "</div>"
    ]
   }
@@ -1543,7 +1545,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.8.10"
   },
   "toc": {
    "base_numbering": 1,

guide/14-deep-learning/track_objects_using_SiamMask.ipynb renamed to guide/14-deep-learning/track_objects_using_siammask.ipynb

@@ -480,9 +480,22 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

items_metadata.yaml

@@ -40,7 +40,7 @@ samples:
   licenseInfo: ""
   tags: ["Data Science", "GIS", "airbnb"]
 - title: Analyzing violent crime
-  url: https://www.arcgis.com/home/home/item.html?id=f19cbf3595de4bf898e7228a46b79ffd
+  url: https://www.arcgis.com/home/item.html?id=f19cbf3595de4bf898e7228a46b79ffd
   path: ./samples/04_gis_analysts_data_scientists/analyzing_violent_crime.ipynb
   thumbnail: ./static/thumbnails/analyzing_violent_crime.png
   snippet: Analyze violent crime in Chicago
@@ -816,6 +816,42 @@ samples:
 #   licenseInfo: ""
 #   runtime: advanced
 #   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
+# - title: Glacial Terminus Extraction using HRNet
+#   url: https://www.arcgis.com/home/item.html?id=e4ee1dbbfdb44853b5cfaef8b2789f7e
+#   path: ./samples/04_gis_analysts_data_scientists/glacial_terminus_extraction_using_hrnet.ipynb
+#   thumbnail: ./static/thumbnails/default.png
+#   snippet: This notebook presents the use of an HRNet model from the arcgis.learn module to accomplish the first task of segmenting calving fronts.
+#   description: This notebook presents the use of an HRNet model from the arcgis.learn module to accomplish the first task of segmenting calving fronts.
+#   licenseInfo: ""
+#   runtime: advanced
+#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
+# - title: Finetuning Pre-trained Building Footprint Model
+#   url: https://www.arcgis.com/home/item.html?id=c90460e8950a4a7e98c2c060deeb5c10
+#   path: ./samples/04_gis_analysts_data_scientists/finetuning_pre-trained_building_footprint_model.ipynb
+#   thumbnail: ./static/thumbnails/default.png
+#   snippet: In this workflow, we will load the training data, finetune a pre-trained model and extract footprints.
+#   description: In this workflow, we will load the training data, finetune a pre-trained model and extract footprints.
+#   licenseInfo: ""
+#   runtime: advanced
+#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
+# - title: Multi-class change detection using image segmentation deep learning models
+#   url: https://www.arcgis.com/home/item.html?id=b478f6cc89554986a955999054db59c4
+#   path: ./samples/04_gis_analysts_data_scientists/multi_class_change_detection_using_segmentation_deep_learning_models.ipynb
+#   thumbnail: ./static/thumbnails/default.png
+#   snippet: In this notebook, we will show a novel way to detect and classify change using semantic segmentation models available in arcgis.learn.
+#   description: In this notebook, we will show a novel way to detect and classify change using semantic segmentation models available in arcgis.learn.
+#   licenseInfo: ""
+#   runtime: advanced
+#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
+# - title: Classification of SfM-derived point clouds using deep learning
+#   url: https://www.arcgis.com/home/item.html?id=f39a0594e523450b898d312dcf72badc
+#   path: ./samples/04_gis_analysts_data_scientists/classification_of_sfm_derived_point_clouds_using_deep_learning.ipynb
+#   thumbnail: ./static/thumbnails/default.png
+#   snippet: In this sample notebook, we will be using the term: 'SfM-derived point clouds' with context to describe point clouds generated from ESRI Site Scan for ArcGIS and ArcGIS Drone2Map.
+#   description: In this sample notebook, we will be using the term: 'SfM-derived point clouds' with context to describe point clouds generated from ESRI Site Scan for ArcGIS and ArcGIS Drone2Map.
+#   licenseInfo: ""
+#   runtime: advanced
+#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
 guides: []
 labs:
 - title: Create Data

samples/04_gis_analysts_data_scientists/finetuning_pre-trained_building_footprint_model.ipynb

@@ -673,27 +673,6 @@
    "source": [
     "<center>A subset of detected building footprints"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -712,7 +691,20 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.8.10"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,

samples/04_gis_analysts_data_scientists/glacial_terminus_extraction_using_hrnet.ipynb

@@ -874,7 +874,7 @@
    "source": [
     "The gif below was achieved with the model trained in this notebook and visualizes the segmented calving front for Rink Isbrae, a major West Greenland outlet glacier.\n",
     "\n",
-    "<p align=\"center\"><img src=\"../../static/img/Glacier_tips.gif\" /></p>"
+    "<p align=\"center\"><img src=\"../../static/img/glacier_tips.gif\" /></p>"
    ]
   },
   {
@@ -908,7 +908,20 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.10"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,