updated notes per suggestions

Author: jyaistMap

guide/02-api-overview/release-notes.ipynb

@@ -29,18 +29,20 @@
     "* [Detecting and Categorizing Brick Kilns from Satellite Imagery](https://developers.arcgis.com/python/samples/04_gis_analysts_data_scientists/detecting_and_categorizing_brick_kilns_from_satellite_imagery.ipynb)\n",
     "* [Extracting Slums from Satellite Imagery](https://developers.arcgis.com/python/samples/04_gis_analysts_data_scientists/extracting_slums_from_satellite_imagery.ipynb)\n",
     "* [Extracting Sinkholes from Aerial Imagery](https://developers.arcgis.com/python/samples/04_gis_analysts_data_scientists/extracting_sinkholes_from_aerial_imagery_over_deadsea)\n",
-    "* [Climate Change Impacts on Trees in Delhi](https://developers.arcgis.com/python/samples/04_gis_analysts_data_scientists/climate_change_impacts_on_trees_in_delhi)\n",
     "* [Detecting Settlements Using Supervised Classification and Deep Learning](https://developers.arcgis.com/python/samples/04_gis_analysts_data_scientists/detecting_settlements_using_landsat-8_imagery)\n",
+    "* [How much green is Delhi as on 15 Oct 2017?](https://developers.arcgis.com/python/samples/04_gis_analysts_data_scientists/how-much-green-is-Delhi-as-on-15-oct-2017) \n",
     "* [Maximizing Fire Protection Coverage](https://developers.arcgis.com/python/samples/04_gis_analysts_data_scientists/site_maximizes_fire_protection_coverage)\n",
     "* [Calculate Impervious Surfaces from Multispectral Imagery using Deep Learning](https://developers.arcgis.com/python/samples/04_gis_analysts_data_scientists/calculate_impervious_surfaces_from_multispectral_imagery/)\n",
-    "* [Time Series Prediction of AirBnB Properties in New York City](https://developers.arcgis.com/python/samples/04_gis_analysts_data_scientists/time_series_prediction_airbnb_properties-nyc)"
+    "* [Safe Streets to Schools](https://developers.arcgis.com/python/samples/04_gis_analysts_data_scientists/safe_streets_to_schools)\n",
+    "* [Spatial and temporal distribution of service calls using big data tools](https://developers.arcgis.com/python/samples/04_gis_analysts_data_scientists/spatial_and_temporal_trends_of_service_calls)\n",
+    "* [Time Series Prediction of AirBnB Properties in New York City](https://developers.arcgis.com/python/samples/04_gis_analysts_data_scientists/analyzing_growth_factors_of_airbnb_properties_in_new_york_city)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### [`arcgis.raster.functios.gbl`](https://developers.arcgis.com/python/api-reference/arcgis.raster.functions.gbl.html#)"
+    "#### [`arcgis.raster.functions.gbl`](https://developers.arcgis.com/python/api-reference/arcgis.raster.functions.gbl.html#)"
    ]
   },
   {
@@ -78,19 +80,12 @@
    "source": [
     "* Adds the [`DeepLabV3`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#deeplab) model based on [`torchvision`](https://pytorch.org/docs/stable/torchvision/index.html)\n",
     " * includes multispectral data support\n",
-    "* Adds [`PointCNN`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#pointcnn) model to segment point clouds into classes\n",
-    "* Adds support for multi-channel data augmentation for training on multispectral imagery\n",
-    " * set `lighting_transforms` parameter in [`prepare_data()`]() to `True` or `False` to control data augmentation\n",
+    "* Enhancements to Multispectral data support: \n",
     " * turn on `DRA` with the `statistics_type` parameter in various `show_results()` and `show_batch()` functions\n",
     " * adds environment variable `type_init_tail_parameters` to control `Model Tail` initialization for `arcgis.learn` functions\n",
     "* Sanctions [`classify_objects()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#classify-objects) over [`FeatureClassifier.categorize_features()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#arcgis.learn.FeatureClassifier.categorize_features) for feature categorization\n",
     "* Adds support for training models on either cpu or gpu devices\n",
     "* Adds support for evaluating MaskRCNN model performance with correct metrics for the trained model to compare the results\n",
-    "* Enhances module to create pre-trained backbones on satellite imagery\n",
-    " * use custom pretrained backbones \n",
-    " * Esri World Imagery\n",
-    " * landsat imagery\n",
-    " * sentinel imagery\n",
     "* Adds [`accuracy()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#arcgis.learn.UnetClassifier.accuracy) function to [`UnetClassifier`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#unetclassifier)\n",
     "* Adds `unet_aux_loss` parameter to the [`PSPNetClassifier`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#pspnetclassifier)\n",
     "* Adds support for training a subset of classes from [`prepare_data()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#prepare-data) `class_mapping` parameter to [`MaskRCNN`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#maskrcnn)\n",
@@ -177,7 +172,6 @@
     "* Improves messaging when incorrect path passed as `path` argument to [`prepare_data()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#prepare-data)\n",
     "* Fixes error when list of tensors is empty when running [`SingleShotDetector.fit()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#arcgis.learn.SingleShotDetector.fit) model\n",
     "* Fixes model accuracy function in [`Unet`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#unetclassifier) and [`PSPNet`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#pspnetclassifier) to return maximum accuracy if checkpoint is `True`\n",
-    "* Enhances [`SingleShotDetector`]() to support rotated bounding boxes as inputs\n",
     "* Improves tagging scheme documentation for [`prepare_data()`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#prepare-data) function\n",
     "* Improves visual accuracy when using Multispectral imagery with [`UNetClassifier`](https://developers.arcgis.com/python/api-reference/arcgis.learn.html#unetclassifier)\n",
     "* Fixes missing `supported_backbone` documentation for all models\n",