From cbc425057b73687d90a5f6d18dd6a44af4d47aa2 Mon Sep 17 00:00:00 2001 From: "Renato L. de F. Cunha" Date: Thu, 26 Oct 2023 15:42:48 -0300 Subject: [PATCH] Fix workflow description in classification.md --- .../data_processing/heatmap/classification.md | 57 +++++++++++-------- 1 file changed, 32 insertions(+), 25 deletions(-) diff --git a/docs/source/docfiles/markdown/workflow_yaml/data_processing/heatmap/classification.md b/docs/source/docfiles/markdown/workflow_yaml/data_processing/heatmap/classification.md index b204d9c1..6742640c 100644 --- a/docs/source/docfiles/markdown/workflow_yaml/data_processing/heatmap/classification.md +++ b/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] -``` \ No newline at end of file +```