{"id":1206402,"date":"2021-05-17T13:29:27","date_gmt":"2021-05-17T20:29:27","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=1206402"},"modified":"2021-05-17T13:55:31","modified_gmt":"2021-05-17T20:55:31","slug":"deep-learning-with-arcgis-pro-part-3-qa-qc-extracted-features","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/deep-learning-with-arcgis-pro-part-3-qa-qc-extracted-features","title":{"rendered":"Deep Learning with ArcGIS Pro Part 3: QA\/QC Extracted Features"},"author":19291,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[22931,22941],"tags":[42181,186132,665211,759942,155352],"industry":[],"product":[36561],"class_list":["post-1206402","blog","type-blog","status-publish","format-standard","hentry","category-imagery","category-mapping","tag-arcgis-pro","tag-deep-learning","tag-geoai","tag-qa-qc","tag-quality-assurance","product-arcgis-pro"],"acf":{"related_articles":[{"ID":1071621,"post_author":"19291","post_date":"2023-11-15 18:05:31","post_date_gmt":"2023-11-16 02:05:31","post_content":"","post_title":"Deep Learning with ArcGIS Pro Tips & Tricks: Part 1","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"deep-learning-with-arcgis-pro-tips-tricks","to_ping":"","pinged":"","post_modified":"2023-11-15 09:05:39","post_modified_gmt":"2023-11-15 17:05:39","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=1071621","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"1","filter":"raw"},{"ID":1152172,"post_author":"19291","post_date":"2021-03-02 01:00:52","post_date_gmt":"2021-03-02 09:00:52","post_content":"","post_title":"Deep Learning with ArcGIS Pro Tips & Tricks: Part 2","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"deep-learning-with-arcgis-pro-tips-tricks-part-2","to_ping":"","pinged":"","post_modified":"2021-10-07 09:27:18","post_modified_gmt":"2021-10-07 16:27:18","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=1152172","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"2","filter":"raw"},{"ID":1035891,"post_author":"6911","post_date":"2020-10-13 11:49:30","post_date_gmt":"2020-10-13 18:49:30","post_content":"","post_title":"Introducing pretrained geospatial deep learning models","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"introducing-ready-to-use-deep-learning-models","to_ping":"","pinged":"","post_modified":"2021-11-19 09:09:52","post_modified_gmt":"2021-11-19 17:09:52","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=1035891","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":1147822,"post_author":"8452","post_date":"2021-02-23 19:27:02","post_date_gmt":"2021-02-24 03:27:02","post_content":"","post_title":"Pretrained deep learning models update (February 2021)","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"announcing-new-pretrained-models-at-fedgis","to_ping":"","pinged":"","post_modified":"2021-11-19 08:58:54","post_modified_gmt":"2021-11-19 16:58:54","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=1147822","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"}],"short_description":"Run Quality Assurance\/ Quality Control (QA\/QC) workflows on your inferenced features such as building footprints.","flexible_content":[{"acf_fc_layout":"content","content":"

In parts one<\/a> and\u00a0two<\/a> of this blog series, you learned how to prepare your environment for deep learning and best practices for using Esri’s pre-trained deep learning models. Now that you have run a model, you are ready to clean up your extracted features by removing objects misidentified as buildings and regularizing your final building polygons.<\/p>\n

Two notes before we get into the tools:<\/p>\n

    \n
  1. In this blog post we will focus on the output features from the Building Footprint Extraction model, but this workflow can also be adapted for other feature extraction model outputs.<\/li>\n
  2. We will be going over some of the most useful tools that are available to refine your detected buildings output, but note that there are multiple workflows for editing features so this is not an exhaustive list.<\/li>\n<\/ol>\n

    Combine Multiple Outputs<\/h3>\n

    In some workflows, you will run the Detect Objects Using Deep Learning geoprocessing tool more than once and need to combine multiple output layers to get your final building polygons. This can be accomplished with the Merge and Dissolve tools as detailed below. If you only have one output from the Detect Objects tool, you can skip these steps and proceed to clipping with parcel data.<\/p>\n

      \n
    • Open your geoprocessing tools and type Merge<\/u> in the search bar.<\/li>\n
    • Open Merge (Data Management Tools)<\/strong>.<\/li>\n
    • Add all the inferenced layers to the Input Datasets<\/strong> variable (select in the Contents<\/strong> pane then drag and drop).<\/li>\n
    • Edit the Output Dataset<\/strong> if needed (note that this is an intermediate layer that you may not need after finishing your QA\/QC workflow).<\/li>\n
    • Leave the other default parameters and run the tool.<\/li>\n<\/ul>\n

      Some buildings were likely identified in both your output results. In order to avoid duplicate features, we will dissolve the boundaries between overlapping building polygons to form one polygon for the building.<\/p>\n

        \n
      • Open your geoprocessing tools and type Dissolve Boundaries<\/u> in the search bar.<\/li>\n
      • Open Dissolve (GeoAnalytics Desktop Tools)<\/strong>.<\/li>\n
      • Add the output layer from the merge tool above as an Input Feature.<\/li>\n
      • Edit the Output Dataset<\/strong> if needed.<\/li>\n
      • Leave the other default parameters and run the tool.<\/li>\n<\/ul>\n

        Split up Multipart Features<\/h3>\n

        After completing the steps above for merging and dissolving together multiple building layers, you may notice that in some cases multiple polygons are being grouped together as one building. To ensure that we get each detected building as a single polygon feature, we will run the Multipart to Singlepart tool.<\/p>\n

          \n
        • Open your geoprocessing tools and type\u00a0Multipart to Singlepart<\/u>\u00a0in the search bar.<\/li>\n
        • Open\u00a0Multipart to Singlepart (Data Management Tools)<\/strong>.<\/li>\n
        • Add the layer derived from the previous step as an Input Feature.<\/li>\n
        • Edit the\u00a0Output Dataset<\/strong>\u00a0if needed (note that this is an intermediate layer that you might not need after finishing your workflow).<\/li>\n
        • Run the tool.<\/li>\n<\/ul>\n

          Clip to Parcel Data<\/strong><\/h3>\n

          If you have land parcel polygon data, you can use it to refine your results by removing any “buildings” that we’re identifies by the model but fall outside of the known parcel boundaries. Assuming the parcel data is up to date, buildings outside these bounds are likely false identificcations. If you do not have parcel data, you can skip this step.<\/p>\n

            \n
          • Open your geoprocessing tools and type Clip<\/u> in the search bar.<\/li>\n
          • Open Clip (Analysis Tools)<\/strong>.<\/li>\n
          • Add your buildings layer as an Input Feature.<\/li>\n
          • Add the parcels data as a clip feature<\/li>\n
          • Edit the Output Dataset<\/strong> if needed (note that this is an intermediate layer that you might not need after finishing your workflow).<\/li>\n
          • Run the tool.<\/li>\n<\/ul>\n

            Remove Misidentified Objects<\/h3>\n

            As described earlier in this process, the deep learning algorithm is never going to output a perfect result. Therefore some smaller features such as cars or piles of construction materials can be identified as buildings. We can remove these non-building polygons based on their area, as their footprints will be often be significantly smaller than a building footprint. In this case we are deleting features smaller than 50 square meters, but this value should be adapted based on the typical size of the object you are looking to detect.<\/p>\n

              \n
            • From the Map<\/strong> tab, under Selection<\/strong>, click Select by Attribute<\/strong>.<\/li>\n
            • Add the layer derived from the previous step as an Input Rows.<\/li>\n
            • Leave the Selection type as New selection<\/strong><\/li>\n
            • Click + New Expression<\/strong>.<\/li>\n
            • Click the SQL<\/strong> button on the right above the table\u00a0to toggle the SQL expression entry<\/li>\n
            • Enter: Shape_Area < 50<\/u>. <\/em><\/li>\n
            • Click OK. <\/strong>Now any features under 50 square meters are selected.<\/li>\n
            • Click the Edit<\/strong> tab, and under Features<\/strong>, click Delete<\/strong>. This will delete your currently selected features.<\/li>\n
            • Click Save <\/strong>to save your edits.<\/li>\n<\/ul>\n"},{"acf_fc_layout":"image","image":{"ID":1206422,"id":1206422,"title":"Irregularized Buildings Footprints","filename":"Irregularized-buildings.jpg","filesize":205135,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/Irregularized-buildings.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/deep-learning-with-arcgis-pro-part-3-qa-qc-extracted-features\/irregularized-buildings","alt":"Irregularized Buildings Footprints","author":"19291","description":"","caption":"Irregularized Buildings Footprints","name":"irregularized-buildings","status":"inherit","uploaded_to":1206402,"date":"2021-04-21 09:34:15","modified":"2021-04-21 09:35:11","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1666,"height":958,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/Irregularized-buildings-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/Irregularized-buildings.jpg","medium-width":454,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/Irregularized-buildings.jpg","medium_large-width":768,"medium_large-height":442,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/Irregularized-buildings.jpg","large-width":1666,"large-height":958,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/Irregularized-buildings-1536x883.jpg","1536x1536-width":1536,"1536x1536-height":883,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/Irregularized-buildings.jpg","2048x2048-width":1666,"2048x2048-height":958,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/Irregularized-buildings-809x465.jpg","card_image-width":809,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/Irregularized-buildings.jpg","wide_image-width":1666,"wide_image-height":958}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

              Regularize Buildings Footprints<\/h3>\n

              As you can see in the image above, the shapes of our current building footprints are irregular, lacking the straight sides and right angles typical of buildings. ArcGIS Pro has a geoprocessing tool specifically designed to clean up these outputs and give you more realistic building footprint polygons:<\/p>\n

                \n
              • Open your geoprocessing tools and type Regularize Buildings Footprints<\/u> in the search bar.<\/li>\n
              • Open Regularize Buildings Footprints (3d Analyst Tools)<\/strong>.<\/li>\n
              • Add the layer derived from the previous step as an Input Feature.<\/li>\n
              • Edit the Output Dataset<\/strong> if needed (note that this is an intermediate layer that you might not need after finishing your workflow).<\/li>\n
              • Leave the Method<\/strong> as default, Right Angles<\/strong>.<\/li>\n
              • Specify a Tolerance<\/strong> In this exercise, we used a value of 5,<\/strong> which means a segment of the extracted polygon needs to be a minimum of 5 meters in length to be identified as a wall.<\/li>\n
              • Leave the other values as default.<\/li>\n
              • Run the tool.<\/li>\n<\/ul>\n

                For an additional challenge, you can calculate the typical ratio of length to width of a building in your image, and use this metric to further identify polygons that are not likely to be buildings.<\/p>\n

                  \n
                • Use the Bounding Geometry <\/strong>geoprocessing tool to create a rectangle around each feature.<\/li>\n
                • Get the ratio of the length to width of the rectangle (filter out any anomalies that get a width that is much bigger than a length; this is good to filter out features that don\u2019t have a logical footprint of a building).<\/li>\n
                • Select and delete these features.<\/li>\n<\/ul>\n

                  Automate the QA\/QC process<\/h2>\n

                  For those who enjoy working with Python, the following is a code snippet that will do the above job for you:<\/p>\n"},{"acf_fc_layout":"sidebar","content":"","image_reference":false,"layout":"code_snippet","image_reference_figure":"","snippet":"#dependent libraries\r\nimport arcpy\r\nimport os\r\n\r\n#import your imagery path. This can be a standalone raster or a mosaic dataset\r\nimagery = r\"\"\r\n\r\n#path to the downloaded dlpk package.\r\ndlModel = r\"\"\r\n\r\n#path to environment file. (Location of your ArcGIS Pro Project and geodatabase)\r\nlocalFile = r\"\"\r\n\r\n#ArcGIS Pro Project default geodatabase name\r\ngdb = \"temp.gdb\"\r\n\r\n#Paddings to run the model\r\npaddings = [\"8\", \"12\", \"16\", \"24\", \"32\"]\r\n\r\n#inferenced features merged into one output path\r\nmergedOutput = os.path.join(localGdb,\"mergedoutput\")\r\n#Dissolved features output path\r\ndissolvedOutput = os.path.join(localGdb,\"dissolvedoutput\")\r\n#multi part polygon output path\r\nmultiPartOutput = os.path.join(localGdb,\"multipartoutput\")\r\n#setting up the workspace environment\r\narcpy.env.workspace = localFile\r\n\r\n#Temp outputs list (mergedOutput, dissolvedOutput, multiPartOutput, inferenced data)\r\ntempOutputs = []\r\n\r\n#looping through the set paddings \r\nfor padding in paddings:\r\n #setting u the model variables\r\n modelVariables = f'padding {padding};batch_size 16;threshold 0.9;return_bboxes False'\r\n output = os.path.join(localGdb, \"output\" +f\"{padding}\")\r\n tempOutputs.append(output)\r\n #inferencing with detect obects using deep learning tool\r\n with arcpy.EnvManager(processorType=\"GPU\"):\r\n arcpy.ia.DetectObjectsUsingDeepLearning(imagery, output, dlModel, modelVariables,\r\n \"NMS\", \"Confidence\", \"Class\", 0, \"PROCESS_AS_MOSAICKED_IMAGE\")\r\n \r\n#merging the inferenced outputs\r\narcpy.management.Merge(tempOutputs, mergedOutput)\r\n#dissolving the boundaries of the merged outputs\r\narcpy.management.Dissolve(mergedOutput, dissolvedOutput)\r\n#creating separate polugon features for each detected building\r\narcpy.management.MultipartToSinglepart(dissolvedOutput, multiPartOutput)\r\n\r\n# Removing buildings with an area less than 50 square meters size\r\ncursor = arcpy.da.UpdateCursor(multiPartOutput, \"SHAPE@AREA\")\r\nfeaturesToDelete = (feature for feature in cursor if feature[0] < 50)\r\nfor row in featuresToDelete:\r\n cursor.deleteRow()\r\ndel cursor\r\n\r\n#Regularizing the final building output\r\narcpy.ddd.RegularizeBuildingFootprint(multiPartOutput, "detected_buildings_automated", "RIGHT_ANGLES",\r\n 5, None, 0.25, 1.5, 0.1, 1000000)","spotlight_name":"","section_title":"","position":"Center","spotlight_image":false}],"authors":[{"ID":19291,"user_firstname":"Rami","user_lastname":"Alouta","nickname":"Rami Alouta","user_nicename":"ralouta","display_name":"Rami Alouta","user_email":"RAlouta@esri.com","user_url":"","user_registered":"2020-03-23 16:57:54","user_description":"Rami is a Solution Engineer on the National Government team supporting nonprofit global organizations and land administration teams out of the Rotterdam office. He has over 5 years of GIS experience and has been working with Esri since 2016 previously as a Platform Configuration Engineer with Professional Services out of the Dubai office. He has a degree in Landscape Architecture from the American University of Beirut.","user_avatar":""},{"ID":137201,"user_firstname":"Kate","user_lastname":"Hess","nickname":"Kate Hess","user_nicename":"khess","display_name":"Kate Hess","user_email":"khess@esri.com","user_url":"","user_registered":"2020-12-09 13:57:59","user_description":"Kate is a Business Development Manager on Esri's National Government team. Based in New York City, she has a background in remote sensing and environmental science. Kate currently supports National Statistics Offices globally, helping them modernize their census and statistics operations using GIS.","user_avatar":""}],"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/banner-3-card.png","wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/banner-3-1.jpg"},"yoast_head":"\nDeep Learning with ArcGIS Pro Part 3: QA\/QC Extracted Features<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/mapping\/deep-learning-with-arcgis-pro-part-3-qa-qc-extracted-features\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep Learning with ArcGIS Pro Part 3: QA\/QC Extracted Features\" \/>\n<meta property=\"og:url\" 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