Fix workflow description in classification.md

docs/source/docfiles/markdown/workflow_yaml/data_processing/heatmap/classification.md

@@ -46,30 +46,37 @@ edges:
   destination:
   - soil_sample_heatmap.raster
 description:
-  short_description: "Utilizes input Sentinel-2 satellite imagery & the sensor samples\
-    \ as labeled data that contain\nnutrient information (Nitrogen, Carbon, pH, Phosphorus)\
-    \ to train a model using Random Forest classifier.\nThe inference operation predicts\
-    \ nutrients in soil for the chosen farm boundary.\n\nThe workflow generates a\
-    \ heatmap for selected nutrient. It relies on sample soil data that contain information\
-    \ of nutrients.\nThe quantity of samples define the accuracy of the heat map generation.\
-    \ During the research performed testing with\nsamples spaced at 200 feet, 100\
-    \ feet and 50 feet. The 50 feet sample spaced distance provided results matching\
-    \ to the\nground truth. Generating heatmap with this approach reduce the number\
-    \ of samples.\nIt utilizes the logic below behind the scenes to generate heatmap.\n\
-    \  - Read the sentinel raster provided.\n  - Sensor samples needs to be uploaded\
-    \ into prescriptions entity in Azure data manager for Agriculture (ADMAg). ADMAg\
-    \ is having hierarchy\n  to hold information of Farmer, Field, Seasons, Crop,\
-    \ Boundary etc. Prior to uploading prescriptions, it is required to build hierarchy\
-    \ and\n  a prescription_map_id. All prescriptions uploaded to ADMAg are related\
-    \ to farm hierarchy through prescription_map_id. Please refer to\n  https://learn.microsoft.com/en-us/rest/api/data-manager-for-agri/\
-    \ for more information on ADMAg.\n  - Compute indices using the spyndex python\
-    \ package.\n  - Clip the satellite imagery & sensor samples using farm boundary.\n\
-    \  - Perform spatial interpolation to find raster pixels within the offset distance\
-    \ from sample location and assign the value of nutrients to group of pixels.\n\
-    \  - Classify the data based on number of bins.\n  - Train the model using Random\
-    \ Forest classifier.\n  - Predict the nutrients using the satellite imagery.\n\
-    \  - Generate a shape file using the predicted outputs."
-  long_description: null
+  short_description: >
+    Utilizes input Sentinel-2 satellite imagery & the sensor samples
+    as labeled data that contain\nnutrient information (Nitrogen, Carbon, pH, Phosphorus)
+    to train a model using Random Forest classifier.\nThe inference operation predicts
+    nutrients in soil for the chosen farm boundary.
+  long_description: |-
+    The workflow generates a heatmap for selected nutrient. It relies on sample
+    soil data that contain information of nutrients.
+    The quantity of samples define the accuracy of the heat map generation.
+    During the research performed testing with\nsamples spaced at 200 feet, 100
+    feet and 50 feet. The 50 feet sample spaced distance provided results matching
+    to the ground truth.
+    Generating heatmaps with this approach reduces the number of samples.
+    It utilizes the logic below behind the scenes to generate heatmap.
+      - Read the sentinel raster provided.
+      - Sensor samples needs to be uploaded into prescriptions entity in Azure
+        data manager for Agriculture (ADMAg). ADMAg is having hierarchy to hold
+        information of Farmer, Field, Seasons, Crop, Boundary etc. Prior to
+        uploading prescriptions, it is required to build hierarchy and
+        a `prescription_map_id`. All prescriptions uploaded to ADMAg are
+        related to farm hierarchy through `prescription_map_id`. Please refer to
+        https://learn.microsoft.com/en-us/rest/api/data-manager-for-agri/ for
+        more information on ADMAg.
+      - Compute indices using the spyndex python package.
+      - Clip the satellite imagery & sensor samples using farm boundary.
+      - Perform spatial interpolation to find raster pixels within the offset distance
+        from sample location and assign the value of nutrients to group of pixels.
+      - Classify the data based on number of bins.
+      - Train the model using Random Forest classifier.
+      - Predict the nutrients using the satellite imagery.
+      - Generate a shape file using the predicted outputs.
   sources:
     input_raster: Input raster for index computation.
     samples: External references to sensor samples for nutrients.
@@ -118,4 +125,4 @@ description:
     inp1>input_raster] -- raster --> tsk1{{compute_index}}
     inp2>samples] -- samples --> tsk2{{soil_sample_heatmap}}
     tsk2{{soil_sample_heatmap}} -- result --> out1>result]
-```
+```