Merge pull request #1805 from jyaistMap/jy-upd-samples-mdata

Update online samples and align metadata with file names

Author: jyaistMap

items_metadata.yaml

@@ -116,7 +116,7 @@ samples:
 #   tags: ["Data Science", "GIS", "Thomas Fire", "California"]
 - title: Chennai Floods 2015 - A Geographic Analysis
   url: https://www.arcgis.com/home/item.html?id=44c4fc1e56654768840a03971feb1e77
-  path: ./samples/04_gis_analysts_data_scientists/chennai_floods_analysis.ipynb
+  path: ./samples/04_gis_analysts_data_scientists/chennai_floods_analysis_rn.ipynb
   thumbnail: ./static/thumbnails/chennai_floods_analysis.jpg
   snippet: Analyze the rainfall in Chennai, India
   description: This sample showcases not just the analysis and visualization capabilities of your GIS, but also the ability to store illustrative text, graphics and live code in a Jupyter notebook.
@@ -320,9 +320,9 @@ samples:
 #   description: Use the ArcGIS API for Python to answer if wildfires are increasing over time.
 #   licenseInfo: ""
 #   tags: ["Data Science", "GIS", "Wildfire"]
-- title: How much green is Delhi as on 15 Oct 2017?
+- title: How much green is Delhi as on 21 Oct 20122?
   url: https://www.arcgis.com/home/item.html?id=8094be16f34e46e48880883a1ae6a4f1
-  path: ./samples/04_gis_analysts_data_scientists/how-much-green-is-Delhi-as-on-15-oct-2017.ipynb
+  path: ./samples/04_gis_analysts_data_scientists/how-much-green-is-Delhi-as-on-21-oct-2022.ipynb
   thumbnail: ./static/thumbnails/how-much-green-is-Delhi-as-on-15-oct-2017.jpg
   snippet: Use Landsat 8 imagery to detect green cover of New Delhi, India
   description: This sample shows the capabilities of spectral indices such as Normalized Difference Vegetation index (NDVI) for the calculation of green cover in Delhi, India on 15 October 2017 using Landsat 8 imagery.
@@ -569,7 +569,7 @@ samples:
   tags: ['Data Science', 'GIS', "Geometry"]
 - title: Using Geoprocessing Tools
   url: https://www.arcgis.com/home/item.html?id=5a5839d87b4645e685bcd46d79995358
-  path: ./samples/02_power_users_developers/using_geoprocessing_tools.ipynb
+  path: ./samples/02_power_users_developers/using_geoprocessing_tools_rn.ipynb
   thumbnail: ./static/thumbnails/using_geoprocessing_tools.png
   snippet: using geoprocessing tools
   description:  The analysis below uses a geoprocessing tool to deduce the path that the debris of a crashed airplane would take if it went down at different places in the ocean.
@@ -771,9 +771,9 @@ samples:
   licenseInfo: ""
   runtime: advanced_gpu
   tags: ["Data Science", "GIS", "Hyperspectral", "Deep Learning"]
-- title: Streams Extraction using MultiTaskRoadExtractor
+- title: Streams Extraction using Deep Learning
   url: https://www.arcgis.com/home/item.html?id=356899f6baad407b9db49bb526073ee1
-  path: ./samples/04_gis_analysts_data_scientists/streams_extraction_using_multi_task_road_extractor.ipynb
+  path: ./samples/04_gis_analysts_data_scientists/streams_extraction_using_deeplearning.ipynb
   thumbnail: ./static/thumbnails/default.png
   snippet: The aim of this notebook is to make use of arcgis.learn MultiTaskRoadExtractor model to extract streams.
   description: The aim of this notebook is to make use of arcgis.learn MultiTaskRoadExtractor model to extract streams.

labs/create_data.ipynb

@@ -236,7 +236,7 @@
   "anaconda-cloud": {},
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "5.0"
+   "notebookRuntimeVersion": "9.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/display_web_map.ipynb

@@ -87,7 +87,7 @@
   "anaconda-cloud": {},
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "5.0"
+   "notebookRuntimeVersion": "9.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/display_web_scene.ipynb

@@ -192,7 +192,7 @@
   "anaconda-cloud": {},
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "5.0"
+   "notebookRuntimeVersion": "9.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/download_data.ipynb

@@ -144,7 +144,7 @@
   "anaconda-cloud": {},
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "5.0"
+   "notebookRuntimeVersion": "9.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/find_places.ipynb

@@ -253,7 +253,7 @@
  "metadata": {
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "5.0"
+   "notebookRuntimeVersion": "9.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/import_data.ipynb

@@ -205,7 +205,7 @@
   "anaconda-cloud": {},
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "5.0"
+   "notebookRuntimeVersion": "9.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/load_item_by_id.ipynb

@@ -134,7 +134,7 @@
  "metadata": {
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "5.0"
+   "notebookRuntimeVersion": "9.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/load_spatial_data_frame.ipynb

@@ -349,7 +349,7 @@
  "metadata": {
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "5.0"
+   "notebookRuntimeVersion": "9.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/search_and_geocode.ipynb

@@ -193,7 +193,7 @@
  "metadata": {
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "5.0"
+   "notebookRuntimeVersion": "9.0"
   },
   "kernelspec": {
    "display_name": "Python 3",